Triple

T2245301
Position Surface form Disambiguated ID Type / Status
Subject Bert Lahr E49488 entity
Predicate role P268 FINISHED
Object Zeke E65703 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: Zeke | Statement: [Bert Lahr, role, Zeke]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Zeke
Context triple: [Bert Lahr, role, Zeke]
  • A. Zeke
    Zeke is the Allied reporting name for the Mitsubishi A6M Zero, the famous Japanese carrier-based fighter aircraft used extensively during World War II.
  • B. Zeke chosen
    Zeke is a central male character in the romantic comedy film "Think Like a Man," known for navigating modern dating dynamics alongside a group of friends influenced by a relationship advice book.
  • C. Zeb
    Zeb is a central character in Margaret Atwood's dystopian novel "MaddAddam," known for his complex past and role in the post-apocalyptic narrative.
  • D. Zan
    Zan is a collective term for a group of closely related Kartvelian languages spoken primarily in western Georgia, including Mingrelian and Laz.
  • E. Dale
    Dale is one half of the classic chipmunk duo Chip 'n' Dale from Disney's Mickey Mouse universe, known for his goofy, fun-loving personality and mischief.
  • 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_69a88aa979788190ad6500f1d8eee2fc completed March 4, 2026, 7:40 p.m.
NER Named-entity recognition batch_69abc0ea75d881909d4e176a432f32e8 completed March 7, 2026, 6:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae6b1284d0819093b041ede90d4c53 completed March 9, 2026, 6:39 a.m.
Created at: March 4, 2026, 7:47 p.m.