Triple

T9566691
Position Surface form Disambiguated ID Type / Status
Subject Standard ML E230804 entity
Predicate hasImplementation P3697 FINISHED
Object Moscow ML
Moscow ML is a lightweight, educationally oriented implementation of the Standard ML programming language, known for its simplicity and support for formal methods and teaching.
E807597 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Moscow ML | Statement: [Standard ML, hasImplementation, Moscow ML]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Moscow ML
Context triple: [Standard ML, hasImplementation, Moscow ML]
  • A. Moscufo
    Moscufo is a small Italian town and comune in the Abruzzo region, noted for its historic architecture and rural setting.
  • B. Moskovici
    Moskovici is a variant spelling of the surname Moskovitz, which is of Eastern European Jewish origin.
  • C. Khimki
    Khimki is a city in Moscow Oblast, Russia, forming part of the Moscow metropolitan area and known for its proximity to major transport hubs and industrial facilities.
  • D. BC Dynamo Moscow
    BC Dynamo Moscow is a professional basketball club from Moscow, Russia, historically associated with the Dynamo sports society and known for competing in national and European competitions.
  • E. MPL
    MPL is the IATA airport code for Montpellier-Méditerranée Airport, serving the city of Montpellier in southern France.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Moscow ML
Triple: [Standard ML, hasImplementation, Moscow ML]
Generated description
Moscow ML is a lightweight, educationally oriented implementation of the Standard ML programming language, known for its simplicity and support for formal methods and teaching.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Moscow ML
Target entity description: Moscow ML is a lightweight, educationally oriented implementation of the Standard ML programming language, known for its simplicity and support for formal methods and teaching.
  • A. Moscufo
    Moscufo is a small Italian town and comune in the Abruzzo region, noted for its historic architecture and rural setting.
  • B. Moskovici
    Moskovici is a variant spelling of the surname Moskovitz, which is of Eastern European Jewish origin.
  • C. Khimki
    Khimki is a city in Moscow Oblast, Russia, forming part of the Moscow metropolitan area and known for its proximity to major transport hubs and industrial facilities.
  • D. BC Dynamo Moscow
    BC Dynamo Moscow is a professional basketball club from Moscow, Russia, historically associated with the Dynamo sports society and known for competing in national and European competitions.
  • E. MPL
    MPL (Mozilla Public License) is a free and open-source software license created by Mozilla that allows code to be shared and modified while requiring that changes to MPL-covered files remain publicly available.
  • F. None of above. chosen

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69ca847f22188190a56e4a97625bef22 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd996df4f08190b19bbaefb10a9789 completed April 1, 2026, 10:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69d152b09c808190aff32419f2cbb15f completed April 4, 2026, 6:04 p.m.
NEDg Description generation batch_69d153d59844819086a0f50e6a7624b2 completed April 4, 2026, 6:09 p.m.
NED2 Entity disambiguation (via description) batch_69d1546a503c81908edc9588adabc172 completed April 4, 2026, 6:11 p.m.
Created at: March 30, 2026, 8:04 p.m.