Triple
T3736319
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seneca Lake |
E79593
|
entity |
| Predicate | numberOfWineriesOnOrNearShore |
P45927
|
FINISHED |
| Object | over 30 |
—
|
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: over 30 | Statement: [Seneca Lake, numberOfWineriesOnOrNearShore, over 30]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfWineriesOnOrNearShore Context triple: [Seneca Lake, numberOfWineriesOnOrNearShore, over 30]
-
A.
approxNumberOfWineries
chosen
Indicates an estimated or approximate count of wineries associated with a given entity.
-
B.
numberOfShoreEstablishmentsInvolved
Indicates the count of shore-based establishments that are involved in or associated with a particular event, operation, or context.
-
C.
hasWinemakingFacility
Indicates that an entity possesses or is associated with a facility where winemaking activities are carried out.
-
D.
hasWinery
Indicates a relationship where a subject owns, operates, or is associated with a particular winery.
-
E.
hasVineyards
Indicates that one entity possesses, contains, or is associated with vineyards used for growing grapevines.
- 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_69ad8b115610819095b02007da5ca3cb |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcb3b399c819091b42209925c0d8f |
completed | March 8, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_69adc04746588190b0dc535638f23546 |
completed | March 8, 2026, 6:30 p.m. |
Created at: March 8, 2026, 3:34 p.m.