Triple

T3875271
Position Surface form Disambiguated ID Type / Status
Subject Lauren Bacall E92485 entity
Predicate spouse P13 FINISHED
Object Humphrey Bogart E14706 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: Humphrey Bogart | Statement: [Lauren Bacall, spouse, Humphrey Bogart]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Humphrey Bogart
Context triple: [Lauren Bacall, spouse, Humphrey Bogart]
  • A. Humphrey Bogart chosen
    Humphrey Bogart was an iconic American film actor best known for his tough yet vulnerable screen persona in classic films such as "Casablanca" and "The Maltese Falcon."
  • B. Belmont DeForest Bogart
    Belmont DeForest Bogart was the father of legendary American film actor Humphrey Bogart and a prominent New York City surgeon.
  • C. Clark Gable
    Clark Gable was a legendary American film actor, best known for his charismatic leading roles in classic Hollywood films such as "Gone with the Wind."
  • D. Gary Cooper
    Gary Cooper was an iconic American film actor renowned for his understated, stoic performances in classic Hollywood films, including major roles in Westerns and dramas.
  • E. Melvyn Douglas
    Melvyn Douglas was an acclaimed American actor known for his sophisticated screen presence and award-winning performances in both classic Hollywood films and later character roles.
  • 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_69aed967448c819086c4b358d37b25aa completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeec706434819095e0d2b376adb548 completed March 9, 2026, 3:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69b52845684c8190b6f0676319a6fc3c completed March 14, 2026, 9:20 a.m.
Created at: March 9, 2026, 3:20 p.m.