Triple

T4768909
Position Surface form Disambiguated ID Type / Status
Subject Tabernacle E105876 entity
Predicate associatedWithPerson P37 FINISHED
Object Moses E11297 NE FINISHED

How this triple was built (2 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: Moses | Statement: [Tabernacle, associatedWithPerson, Moses]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Moses
Context triple: [Tabernacle, associatedWithPerson, Moses]
  • A. Moses chosen
    Moses is a central prophet and leader in the Hebrew Bible, traditionally credited with leading the Israelites out of Egypt and receiving the Ten Commandments from God.
  • B. Mose
    Mose is a minor character in Harriet Beecher Stowe’s novel "Uncle Tom’s Cabin," depicted as one of Uncle Tom’s children within the enslaved family central to the story.
  • C. Mūsa
    Mūsa is a river in Latvia that serves as one of the main tributaries forming the larger Lielupe River.
  • D. Moses the Black
    Moses the Black was a 4th-century Ethiopian desert monk and former bandit who became a renowned Christian ascetic and saint among the Desert Fathers.
  • E. Yoshua
    Yoshua is a male given name most notably borne by Yoshua Bengio, a pioneering Canadian computer scientist and deep learning researcher.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69bd43f226fc8190b867cc249c2a9042 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd6537eb80819096e0ae906c59d605 completed March 20, 2026, 3:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69be3a4e0844819098bb9abb05094a89 completed March 21, 2026, 6:27 a.m.
Created at: March 20, 2026, 1:21 p.m.