Triple
T11934180
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gateway Arts District |
E283996
|
entity |
| Predicate | hasPolicyTool |
P1652
|
FINISHED |
| Object | arts and entertainment district incentives |
—
|
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: arts and entertainment district incentives | Statement: [Gateway Arts District, hasPolicyTool, arts and entertainment district incentives]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPolicyTool Context triple: [Gateway Arts District, hasPolicyTool, arts and entertainment district incentives]
-
A.
hasPolicySupport
Indicates that one entity provides endorsement, backing, or approval for a specific policy associated with another entity.
-
B.
hasPolicyInstrumentType
Indicates that a policy or measure is associated with a specific type or category of policy instrument used to implement it.
-
C.
hasPolicyStatus
Indicates that an entity is associated with a policy and specifies the current status or state of that policy.
-
D.
policyTool
chosen
Indicates that an entity is a tool, mechanism, or instrument used to design, implement, or enforce a policy.
-
E.
analyzesPolicyTool
Indicates that one entity examines, evaluates, or studies a policy-related tool or instrument to understand its features, effectiveness, or implications.
- 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_69d6ab2ce9c48190b5d39511b524f666 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d90306fcf48190a963d2d1932288d1 |
completed | April 10, 2026, 2:02 p.m. |
| PD | Predicate disambiguation | batch_69d8bb3af0188190bfb22be5c97b3349 |
completed | April 10, 2026, 8:56 a.m. |
Created at: April 8, 2026, 9:45 p.m.