Triple

T363676
Position Surface form Disambiguated ID Type / Status
Subject PENN Entertainment E7911 entity
Predicate formerlyKnownAs P65 FINISHED
Object Penn National Gaming E7911 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: Penn National Gaming | Statement: [PENN Entertainment, formerlyKnownAs, Penn National Gaming]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Penn National Gaming
Context triple: [PENN Entertainment, formerlyKnownAs, Penn National Gaming]
  • A. PENN Entertainment chosen
    PENN Entertainment is a major U.S. gaming and entertainment company that operates casinos, racetracks, and online betting platforms across North America.
  • B. Caesars Entertainment
    Caesars Entertainment is a major American gaming and hospitality company that owns and operates numerous casinos, hotels, and resorts across the United States and internationally.
  • C. Wynn Resorts
    Wynn Resorts is a luxury hotel and casino development company known for its high-end integrated resorts in Las Vegas and Macau.
  • D. Empire City Casino
    Empire City Casino is a major gaming and entertainment complex located in Yonkers, just north of New York City.
  • E. Regal Entertainment Group
    Regal Entertainment Group is one of the largest movie theater chains in the United States, operating multiplex cinemas across the country.
  • 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_69a2e7e880008190a6ad7e06e5d03007 completed Feb. 28, 2026, 1:04 p.m.
NER Named-entity recognition batch_69a2ebd1016481909b8ba3b047a47145 completed Feb. 28, 2026, 1:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3e86533a481909bab5f0b52114c6a completed March 1, 2026, 7:19 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.