Triple

T1513837
Position Surface form Disambiguated ID Type / Status
Subject University of Tampa E32073 entity
Predicate city P40 FINISHED
Object Tampa E3075 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: Tampa | Statement: [University of Tampa, city, Tampa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tampa
Context triple: [University of Tampa, city, Tampa]
  • A. Tampa, Florida chosen
    Tampa, Florida is a major city on Florida’s Gulf Coast known for its professional sports teams, port and business center, and role as a key hub in the greater Tampa Bay area.
  • B. Orlando
    Orlando is a major city in central Florida known for its theme parks, tourism industry, and entertainment attractions.
  • C. Orlando
    Orlando is a historic township area within Soweto, South Africa, known for its central role in the anti-apartheid struggle and vibrant local culture.
  • D. St. Petersburg, Florida
    St. Petersburg, Florida is a coastal city on Florida’s Gulf Coast known for its sunny climate, beaches, and vibrant arts and cultural scene.
  • E. Jacksonville, Florida
    Jacksonville, Florida is a major city in northeastern Florida known for its extensive riverfront, large land area, and role as a regional economic and transportation hub.
  • 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_69a885e8caf88190a5fbb6159ce87786 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a907d901ac8190be55ed4bac609d1d completed March 5, 2026, 4:34 a.m.
NED1 Entity disambiguation (via context triple) batch_69af6529905081909bd9e7c51fc21f77 completed March 10, 2026, 12:26 a.m.
Created at: March 4, 2026, 7:26 p.m.