Triple
T33654347
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shiva Theater |
E862181
|
entity |
| Predicate | cityTheaterDistrict |
P84874
|
FINISHED |
| Object | Downtown Manhattan theater district |
—
|
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: Downtown Manhattan theater district | Statement: [Shiva Theater, cityTheaterDistrict, Downtown Manhattan theater district]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cityTheaterDistrict Context triple: [Shiva Theater, cityTheaterDistrict, Downtown Manhattan theater district]
-
A.
theaterDistrict
chosen
Indicates that a location is situated within or associated with a designated theater district.
-
B.
hasTheatreDistrictRole
Indicates that an entity holds a specific role, function, or designation within a theatre district.
-
C.
notableCityInTheater
Indicates that a city holds particular significance or prominence within a specified theater (such as a military, cultural, or operational region).
-
D.
theatreCity
Indicates that a theatre is located in, or primarily associated with, a particular city.
-
E.
theaterSector
Indicates that an entity operates in, is associated with, or belongs to the theater-related sector or industry.
- 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_69f349840ba881908e3bfce536aeb92b |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6fb19063c81909466b329655c8583 |
completed | May 3, 2026, 7:36 a.m. |
| PD | Predicate disambiguation | batch_69f6f96badb08190994442c2aba840b1 |
completed | May 3, 2026, 7:29 a.m. |
Created at: May 1, 2026, 1:42 a.m.