Triple
T19905283
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Regency Theatres |
E478396
|
entity |
| Predicate | typeOfTheatersOperated |
P28912
|
FINISHED |
| Object | neighborhood theaters |
—
|
LITERAL 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: neighborhood theaters | Statement: [Regency Theatres, typeOfTheatersOperated, neighborhood theaters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfTheatersOperated Context triple: [Regency Theatres, typeOfTheatersOperated, neighborhood theaters]
-
A.
hasNumberOfCinemas
Indicates the quantity of cinemas associated with a given entity.
-
B.
hasOperatingTheatres
Indicates that an entity possesses or includes one or more operating theatres as part of its facilities or infrastructure.
-
C.
theaterType
chosen
Indicates the specific kind or category of theater associated with an entity (e.g., cinema, opera house, drama theater).
-
D.
scopeOfTheatres
Indicates that there is a defined range, coverage, or extent of activities, responsibilities, or influence associated with particular theatres (e.g., theaters of operation or venues).
-
E.
hasNumberOfTheatres
Indicates the quantity of theatres associated with or present in a given entity.
- F. None of above.
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_69d8e520682081909892916424699bd5 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65945cc2081908224902d5f042d7a |
completed | April 20, 2026, 4:50 p.m. |
| PD | Predicate disambiguation | batch_69e537ecda248190895c96afb6243823 |
completed | April 19, 2026, 8:15 p.m. |
Created at: April 10, 2026, 1:52 p.m.