Triple
T19819352
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Joe |
E476144
|
entity |
| Predicate | operator |
P179
|
FINISHED |
| Object | Olympia Entertainment |
—
|
NE NERFINISHED |
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: Olympia Entertainment | Statement: [The Joe, operator, Olympia Entertainment]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Olympia Entertainment Context triple: [The Joe, operator, Olympia Entertainment]
-
A.
Olympia Entertainment
chosen
Olympia Entertainment is a Detroit-based sports and entertainment company that manages major venues and events, including professional sports arenas and historic theaters.
-
B.
Regal Entertainment Group
Regal Entertainment Group is one of the largest movie theater chains in the United States, operating multiplex cinemas across the country.
-
C.
Tropicana Entertainment
Tropicana Entertainment was a casino and entertainment company that owned and operated a portfolio of casino resorts and gaming properties in the United States.
-
D.
Woods Entertainment
Woods Entertainment is a film production company best known for producing the crime drama movie "Cop Land."
-
E.
Boyd Gaming
Boyd Gaming is a major American gaming and hospitality company that owns and operates numerous casinos, hotels, and entertainment properties across the United States.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e51c7c188190b926f3a2a7b5f881 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e654fc8b94819095fd5240f33b6713 |
completed | April 20, 2026, 4:31 p.m. |
Created at: April 10, 2026, 1:50 p.m.