Triple
T7608240
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pinot Teinturier |
E180162
|
entity |
| Predicate | synonymType |
P3575
|
FINISHED |
| Object | teinturier Pinot |
—
|
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: teinturier Pinot | Statement: [Pinot Teinturier, synonymType, teinturier Pinot]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: synonymType Context triple: [Pinot Teinturier, synonymType, teinturier Pinot]
-
A.
synonym
chosen
Indicates that two terms have the same or nearly the same meaning in a given context.
-
B.
semanticType
Indicates that something belongs to or is categorized under a particular semantic class or type based on its meaning.
-
C.
symbolType
Indicates the classification or category of a symbol based on its role, form, or function within a given system.
-
D.
termType
Indicates the classification or category of a term within a system, specifying what kind of term it is (e.g., type, role, or function) in relation to others.
-
E.
heteronym
Indicates that two or more words share the same spelling but differ in pronunciation and meaning.
- 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_69c69f3567008190ab01d2ca7b53584a |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c6fa1de8a4819091f9e9347835ce16 |
completed | March 27, 2026, 9:43 p.m. |
| PD | Predicate disambiguation | batch_69c6f4e485f88190910b39da52a955fe |
completed | March 27, 2026, 9:21 p.m. |
Created at: March 27, 2026, 3:54 p.m.