Triple

T8036263
Position Surface form Disambiguated ID Type / Status
Subject Bob Gale E187115 entity
Predicate wrote P2831 FINISHED
Object Used Cars E409583 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: Used Cars | Statement: [Bob Gale, wrote, Used Cars]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Used Cars
Context triple: [Bob Gale, wrote, Used Cars]
  • A. Used Cars chosen
    Used Cars is a 1980 American satirical comedy film starring Kurt Russell as a fast-talking, unscrupulous car salesman embroiled in a rivalry between competing dealerships.
  • B. Cars
    Cars is a 2006 Pixar animated film that follows a hotshot race car who discovers friendship and humility in a forgotten desert town.
  • C. Autotrader
    Autotrader is a major online automotive marketplace where consumers can buy, sell, and research new and used vehicles.
  • D. CAR
    CAR is the Cordillera Administrative Region in the Philippines, an upland area in Northern Luzon known for its mountainous terrain and indigenous cultures.
  • E. CAR
    CAR is a research center dedicated to advancing the understanding, diagnosis, and treatment of autism spectrum disorders through scientific study and clinical collaboration.
  • 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_69ca82ae2d1081909dbfee42b41db419 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3ef68c6081908727d17238b3522a completed March 31, 2026, 3:26 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc56f493908190b68e791cdbe725fa completed March 31, 2026, 11:21 p.m.
Created at: March 30, 2026, 5:22 p.m.