Triple

T12860271
Position Surface form Disambiguated ID Type / Status
Subject Chester Hanks E307566 entity
Predicate givenName P17 FINISHED
Object Chester E61539 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: Chester | Statement: [Chester Hanks, givenName, Chester]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Chester
Context triple: [Chester Hanks, givenName, Chester]
  • A. Chester chosen
    Chester is the given name of Chester W. Nimitz, the prominent U.S. Navy fleet admiral who played a leading role in the Pacific theater during World War II.
  • B. Chester
    Chester is a historic walled city in northwest England known for its Roman heritage, medieval architecture, and distinctive two-tiered shopping galleries called the Rows.
  • C. Chester
    Chester is a historic walled city in northwest England renowned for its Roman heritage, medieval architecture, and well-preserved city walls.
  • D. Chester
    Chester is a historic walled city in northwest England, renowned for its well-preserved Roman and medieval architecture.
  • E. Chester
    Chester is a historic city in northwest England known for its Roman walls, medieval architecture, and distinctive black-and-white timbered buildings.
  • 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_69d7bdf5e7cc8190be357278bc5ba3bb completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d9708ba74881909b16c1e2ef5115db completed April 10, 2026, 9:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69f69b9dc1e48190993430956e0fcfdc completed May 3, 2026, 12:49 a.m.
Created at: April 9, 2026, 5:37 p.m.