Triple
T490577
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vermont |
E9978
|
entity |
| Predicate | hasFamousProduct |
P1448
|
FINISHED |
| Object | maple syrup |
—
|
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: maple syrup | Statement: [Vermont, hasFamousProduct, maple syrup]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFamousProduct Context triple: [Vermont, hasFamousProduct, maple syrup]
-
A.
hasProduct
Indicates that an entity possesses, offers, or is associated with a particular product.
-
B.
notableProduct
chosen
Indicates that a product is especially significant, prominent, or well-known in relation to the associated entity.
-
C.
hasPerson
Indicates that an entity is associated with or includes a specific person.
-
D.
notablyAssociatedWith
Indicates that one entity is prominently or distinctively connected with another in a way that is especially noteworthy or remarkable.
-
E.
hasCulturalProduct
Indicates that an entity possesses, produces, or is associated with a cultural artifact, work, or output (such as art, literature, music, or media).
- 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_69a2e802e2908190ab17c9479e0b6412 |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2f0e22a308190b04d12974fd08a38 |
completed | Feb. 28, 2026, 1:42 p.m. |
| PD | Predicate disambiguation | batch_69a2edf63fbc819090ea6ca11f39116a |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.