Triple

T11054329
Position Surface form Disambiguated ID Type / Status
Subject Yolanda Foster E261335 entity
Predicate modeledFor P2006 FINISHED
Object Ford Models E900056 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: Ford Models | Statement: [Yolanda Foster, modeledFor, Ford Models]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ford Models
Context triple: [Yolanda Foster, modeledFor, Ford Models]
  • A. Ford Models chosen
    Ford Models is a prominent international modeling agency known for representing high-profile fashion models and shaping the modern modeling industry.
  • B. Ford vehicles
    Ford vehicles are a range of automobiles produced by the Ford Motor Company, including cars, trucks, SUVs, and commercial vehicles sold worldwide.
  • C. Ford EXP
    The Ford EXP was a compact, two-seat sport hatchback produced by Ford in the 1980s as a sporty offshoot of the Escort line.
  • D. Ford F-Series
    The Ford F-Series is a long-running line of full-size pickup trucks that has become one of the best-selling and most iconic vehicle ranges in automotive history.
  • E. Ford Fusion
    The Ford Fusion is a mid-size sedan produced by Ford, known for its mainstream appeal, fuel-efficient powertrains, and role as one of the brand’s core passenger cars in the 2000s and 2010s.
  • 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_69d6aa98650481908609c7c56bfa7902 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d798a0890481909c1d4f9d5b23f33a completed April 9, 2026, 12:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69e3c86052148190adfc250c3dd27094 completed April 18, 2026, 6:07 p.m.
Created at: April 8, 2026, 9:26 p.m.