Triple

T4824946
Position Surface form Disambiguated ID Type / Status
Subject Ashmont station E107799 entity
Predicate hasStationCode P1289 FINISHED
Object R2
R2 is the MBTA station code used to identify Ashmont station on Boston's Red Line transit system.
 E473361 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: R2 | Statement: [Ashmont station, hasStationCode, R2]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: R2
Context triple: [Ashmont station, hasStationCode, R2]
  • A. R5
    R5 is the U.S. Forest Service’s Pacific Southwest Region, which oversees national forests primarily in California and parts of neighboring areas.
  • B. R5
    R5 is a government office building in Oslo that forms part of Norway’s central Regjeringskvartalet complex.
  • C. R-4
    The R-4 is a World War II–era Sikorsky helicopter recognized as the first mass-produced helicopter and the first to be used operationally by the U.S. military.
  • D. R-5
    The R-5 is an early Sikorsky military helicopter developed during World War II, known for advancing rotary-wing design and serving in roles such as rescue and liaison.
  • E. R4
    R4 is a government office building in Oslo that forms part of Norway’s central Regjeringskvartalet complex.
  • 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: R2
Triple: [Ashmont station, hasStationCode, R2]
Generated description
R2 is the MBTA station code used to identify Ashmont station on Boston's Red Line transit system.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: R2
Target entity description: R2 is the MBTA station code used to identify Ashmont station on Boston's Red Line transit system.
  • A. R5
    R5 is a government office building in Oslo that forms part of Norway’s central Regjeringskvartalet complex.
  • B. R5
    R5 is the U.S. Forest Service’s Pacific Southwest Region, which oversees national forests primarily in California and parts of neighboring areas.
  • C. R-4
    The R-4 is a World War II–era Sikorsky helicopter recognized as the first mass-produced helicopter and the first to be used operationally by the U.S. military.
  • D. R-5
    The R-5 is an early Sikorsky military helicopter developed during World War II, known for advancing rotary-wing design and serving in roles such as rescue and liaison.
  • E. R4
    R4 is a government office building in Oslo that forms part of Norway’s central Regjeringskvartalet complex.
  • 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_69bd43fac8188190803f0327190621e4 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd6cadb2bc81909455149e46eb593a completed March 20, 2026, 3:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69be4dca2b708190ac05c91ba04d9ff6 completed March 21, 2026, 7:50 a.m.
NEDg Description generation batch_69be4fc6ea3c819099ede84700eb5a5a completed March 21, 2026, 7:59 a.m.
NED2 Entity disambiguation (via description) batch_69be5146f1ac8190977512323af69487 completed March 21, 2026, 8:05 a.m.
Created at: March 20, 2026, 1:24 p.m.