Triple
T6718224
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hollywood Theatre |
E153326
|
entity |
| Predicate | hasProgrammingPartner |
P1136
|
FINISHED |
| Object | local arts organizations |
—
|
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: local arts organizations | Statement: [Hollywood Theatre, hasProgrammingPartner, local arts organizations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProgrammingPartner Context triple: [Hollywood Theatre, hasProgrammingPartner, local arts organizations]
-
A.
hasNetworkPartner
Indicates that an entity is connected to another entity through a formal or recognized network partnership relationship.
-
B.
hasPartner
chosen
Indicates that one entity is in a partner relationship (such as romantic, life, or business partnership) with another entity.
-
C.
hasOnscreenPartner
Indicates that one entity appears together with another as a partner within the same onscreen context or scene.
-
D.
technologyPartner
Indicates a collaborative relationship where one entity provides technological expertise, products, or services to support or co-develop another entity’s solutions or operations.
-
E.
hasProgrammingFrom
Indicates that something derives its programming, configuration, or behavioral instructions from a specified source.
- 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_69c68809b4608190a2509ddb5ab87f05 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d12765a48190b485176dc2ffa0fa |
completed | March 27, 2026, 6:49 p.m. |
| PD | Predicate disambiguation | batch_69c6d08c5d348190a29dee668c398e70 |
completed | March 27, 2026, 6:46 p.m. |
Created at: March 27, 2026, 2:07 p.m.