Triple

T7821740
Position Surface form Disambiguated ID Type / Status
Subject Heinie Zimmerman E181145 entity
Predicate fullName P16 FINISHED
Object Henry Zimmerman E695090 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: Henry Zimmerman | Statement: [Heinie Zimmerman, fullName, Henry Zimmerman]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Henry Zimmerman
Context triple: [Heinie Zimmerman, fullName, Henry Zimmerman]
  • A. Henry Zimmerman chosen
    Henry Zimmerman, better known as Heinie Zimmerman, was an early 20th-century American Major League Baseball player noted for his strong hitting and controversial role in a game-fixing scandal.
  • B. Don Zimmerman
    Don Zimmerman is a film editor known for his work on major Hollywood movies, including the family adventure-comedy "Night at the Museum."
  • C. John Weiss
    John Weiss is a relatively obscure individual whose specific notability is not clearly established from the given information.
  • D. Samuel Diescher
    Samuel Diescher was a prominent 19th-century civil and mechanical engineer known for designing several American inclines and industrial structures, particularly in Pittsburgh.
  • E. Henry Baerer
    Henry Baerer was a German-American sculptor known for creating notable public monuments and statues in the United States during the late 19th century.
  • 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_69ca828153f48190bdb27ac46f8e0745 completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cafa083fe88190a77efb7cfee4bd6f completed March 30, 2026, 10:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69cb5a66c1c0819087ce9890f28bb022 completed March 31, 2026, 5:23 a.m.
Created at: March 30, 2026, 4:41 p.m.