Triple
T8267446
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ed Sullivan Theater |
E193335
|
entity |
| Predicate | landmarkDesignationType |
P55003
|
FINISHED |
| Object | interior landmark |
—
|
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: interior landmark | Statement: [Ed Sullivan Theater, landmarkDesignationType, interior landmark]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: landmarkDesignationType Context triple: [Ed Sullivan Theater, landmarkDesignationType, interior landmark]
-
A.
religiousDesignation
Indicates the specific religious role, status, or affiliation assigned to an entity.
-
B.
stateDesignation
Indicates that an entity has been formally assigned or recognized with a particular status, title, or classification by an official authority or governing body.
-
C.
nationalDesignation
Indicates that an entity has been formally assigned a specific status, title, or classification by a nation or national authority.
-
D.
cityLandmarkDesignation
chosen
Indicates that a particular landmark has been officially designated or recognized as a landmark by a specific city.
-
E.
earthDesignation
Indicates that an entity has a specific name or label assigned to it for use in an Earth-based or human-centric context.
- 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_69ca82e081d48190986beaa51f498ab9 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb794fc4208190b268bc69ff2b28a9 |
completed | March 31, 2026, 7:35 a.m. |
| PD | Predicate disambiguation | batch_69cb36b8707881909aca349230495a5a |
completed | March 31, 2026, 2:51 a.m. |
Created at: March 30, 2026, 5:50 p.m.