Triple

T11013605
Position Surface form Disambiguated ID Type / Status
Subject Ann Dusenberry E260303 entity
Predicate appearedIn P795 FINISHED
Object Cutter to Houston E900094 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: Cutter to Houston | Statement: [Ann Dusenberry, appearedIn, Cutter to Houston]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cutter to Houston
Context triple: [Ann Dusenberry, appearedIn, Cutter to Houston]
  • A. Cutter to Houston chosen
    "Cutter to Houston" is a short-lived 1983 American medical drama television series set in a small Texas town, starring Ann Dusenberry among its lead cast.
  • B. Southwest Houston
    Southwest Houston is a large, diverse residential and commercial area of Houston, Texas, known for its mix of established neighborhoods, shopping centers, and multicultural communities.
  • C. Will Houston
    Will Houston is a British actor known for his roles in film and television, including a prominent appearance in the 2013 TV miniseries "The Bible."
  • D. METRO (Houston)
    METRO (Houston) is the public transportation authority serving the Houston metropolitan area, operating bus, light rail, and related transit services.
  • E. Forney
    Forney is a surname of German origin borne by various notable individuals, including engineers, politicians, and artists.
  • 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_69d6aa9687448190b28d353b1b6a610e completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d7978b1e888190b297f107f6021b59 completed April 9, 2026, 12:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69e3a98725808190903639866a3e745f completed April 18, 2026, 3:55 p.m.
Created at: April 8, 2026, 9:25 p.m.