Triple

T12880853
Position Surface form Disambiguated ID Type / Status
Subject Gainsborough E308087 entity
Predicate notableWork P4 FINISHED
Object The Market Cart E642425 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: The Market Cart | Statement: [Gainsborough, notableWork, The Market Cart]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: The Market Cart
Context triple: [Gainsborough, notableWork, The Market Cart]
  • A. The Market Cart chosen
    The Market Cart is an 18th-century pastoral landscape painting by Thomas Gainsborough, depicting rural life with a cart traveling along a wooded country lane.
  • B. Man Push Cart
    Man Push Cart is a 2005 independent drama film that follows a former Pakistani rock star struggling to make a living as a street vendor in New York City.
  • C. El Mercado
    El Mercado is a vibrant, historic Mexican-style market in San Antonio, Texas, known for its shops, restaurants, and cultural festivities.
  • D. The Boy with a Cart
    The Boy with a Cart is a play by Christopher Fry that dramatizes the life and spiritual journey of the medieval English saint Cuthman of Steyning.
  • E. The Blue Market
    The Blue Market is a historic open-air street market and community hub in the Bermondsey area of south London.
  • 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_69d7bdf69bc48190af6c2621f28ca351 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d970fc1e488190a0c48039f6213e62 completed April 10, 2026, 9:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69f69bbb735c8190a08683a6183e60c4 completed May 3, 2026, 12:50 a.m.
Created at: April 9, 2026, 5:39 p.m.