Triple
T30681570
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SUNY community colleges (in coordination with local sponsors) |
E781064
|
entity |
| Predicate | localSponsorType |
P2589
|
FINISHED |
| Object | counties |
—
|
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: counties | Statement: [SUNY community colleges (in coordination with local sponsors), localSponsorType, counties]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: localSponsorType Context triple: [SUNY community colleges (in coordination with local sponsors), localSponsorType, counties]
-
A.
localSponsor
Indicates that an entity provides sponsorship or support within a specific local area or community.
-
B.
sponsoringOrganizationType
chosen
Indicates the kind or category of organization that provides sponsorship or support in the described relationship or activity.
-
C.
sponsorType
Indicates the specific role or category of sponsorship that an entity provides in relation to another entity or event.
-
D.
sponsorBasedInCity
Indicates that a sponsor’s primary location or headquarters is situated in a specified city.
-
E.
sponsorLocation
Indicates that an entity acts as a sponsor for activities, events, or operations occurring at a specified location.
- 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_69f224a92f54819095499b4d32bd5134 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f6a0ea04888190ac3a813b603bcb5c |
completed | May 3, 2026, 1:12 a.m. |
| PD | Predicate disambiguation | batch_69f69fe463248190aa78128abeab1183 |
completed | May 3, 2026, 1:07 a.m. |
Created at: April 29, 2026, 8:32 p.m.