Triple
T4680345
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Entraygues-le-Fel AOC |
E103782
|
entity |
| Predicate | grapeColorRedDominant |
P45197
|
FINISHED |
| Object | Fer Servadou |
—
|
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: Fer Servadou | Statement: [Entraygues-le-Fel AOC, grapeColorRedDominant, Fer Servadou]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: grapeColorRedDominant Context triple: [Entraygues-le-Fel AOC, grapeColorRedDominant, Fer Servadou]
-
A.
grapeColorForReds
chosen
Indicates that the predicate specifies the typical color of grapes used to produce red wines.
-
B.
grapeColorProduced
Indicates the color that is produced by or characteristic of a given grape.
-
C.
primaryGrapeVariety
Indicates that one entity is the main or predominant grape variety used in producing the other entity (typically a wine or wine-based product).
-
D.
wineColor
Indicates the color attribute or hue associated with a given wine.
-
E.
secondaryGrape
Indicates that one grape variety serves as a secondary or supporting component in a wine blend relative to the primary grape.
- 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_69bd43debbf08190b4bc372e286ec234 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd67c9c3c08190a6c4944cdd1362a8 |
completed | March 20, 2026, 3:29 p.m. |
| PD | Predicate disambiguation | batch_69bd6217e0088190836570522e324dc6 |
completed | March 20, 2026, 3:04 p.m. |
Created at: March 20, 2026, 1:16 p.m.