Triple

T12712267
Position Surface form Disambiguated ID Type / Status
Subject University of Texas at Tyler E303748 entity
Predicate city P40 FINISHED
Object Tyler, Texas E235749 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: Tyler, Texas | Statement: [University of Texas at Tyler, city, Tyler, Texas]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tyler, Texas
Context triple: [University of Texas at Tyler, city, Tyler, Texas]
  • A. Tyler, Texas chosen
    Tyler, Texas is a mid-sized East Texas city known as the “Rose Capital of America” for its large rose industry and annual Texas Rose Festival.
  • B. Taylor, Texas
    Taylor, Texas is a small city in central Texas known for its historic downtown, agricultural roots, and location within the Greater Austin metropolitan area.
  • C. Alexander, Texas
    Alexander, Texas is a small unincorporated rural community located in Erath County in north-central Texas.
  • D. Taft, Texas
    Taft, Texas is a small city in San Patricio County that functions as part of the greater Corpus Christi metropolitan region in South Texas.
  • E. Garland, Texas
    Garland, Texas is a large suburban city in northeastern Texas known for its diverse community, manufacturing base, and role as a major suburb of Dallas within the Dallas–Fort Worth metropolitan area.
  • 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_69d7bdf084148190ab9d513dc0735af4 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d96208fa6481909d6fd43654752a2d completed April 10, 2026, 8:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6cbb4d0088190b71fc0573cd40ddd completed May 3, 2026, 4:14 a.m.
Created at: April 9, 2026, 5:23 p.m.