Triple
T4905936
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LEED for Cities and Communities |
E109911
|
entity |
| Predicate | targetStakeholders |
P10541
|
FINISHED |
| Object | municipal governments |
—
|
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: municipal governments | Statement: [LEED for Cities and Communities, targetStakeholders, municipal governments]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: targetStakeholders Context triple: [LEED for Cities and Communities, targetStakeholders, municipal governments]
-
A.
targetMarket
Indicates the group of consumers or organizations that a product, service, or campaign is specifically intended and designed to reach.
-
B.
targetsGroup
chosen
Indicates that an action, influence, or effect is directed toward a specific group as its intended recipient or focus.
-
C.
targetsUseCase
Indicates that one entity is aimed at or designed to address a particular use case associated with another entity.
-
D.
targetsSector
Indicates that an entity is directed toward, focused on, or intended to affect a particular economic or industry sector.
-
E.
targetOfIntervention
Indicates that an entity is the object or focus upon which an intervention, treatment, or action is directed.
- 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_69bd441180708190ba42ffb44fea533a |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6e72ed5c819081104c99a398e0af |
completed | March 20, 2026, 3:57 p.m. |
| PD | Predicate disambiguation | batch_69bd6c325e188190823836d79934e9bc |
completed | March 20, 2026, 3:48 p.m. |
Created at: March 20, 2026, 1:29 p.m.