Triple
T31704093
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Premier Cru Supérieur (for Sauternes) |
E809133
|
entity |
| Predicate | numberOfEstatesInRank |
P39431
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [Premier Cru Supérieur (for Sauternes), numberOfEstatesInRank, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfEstatesInRank Context triple: [Premier Cru Supérieur (for Sauternes), numberOfEstatesInRank, 1]
-
A.
numberOfEstates
Indicates the total count of estates associated with a given entity or context.
-
B.
originalNumberOfEstates
Indicates the total count of estates that existed initially, before any changes or subsequent events.
-
C.
isOnlyEstateWithRank
Indicates that an estate is the sole estate possessing a particular rank, with no other estate sharing that same rank.
-
D.
numberOfClassifiedEstates
Indicates the count of estates that have been formally classified or categorized in a given context.
-
E.
numberOfRankedEntities
chosen
Indicates the total count of entities that have been assigned a rank within a given ranking or ordered set.
- 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_69f348de914081909fc8edff56f34dbe |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_6a01378bf05c8190af9f5e06a2810c7d |
completed | May 11, 2026, 1:57 a.m. |
| PD | Predicate disambiguation | batch_6a0137277c6c8190bcec341f2a0757c4 |
completed | May 11, 2026, 1:55 a.m. |
Created at: April 30, 2026, 11:13 p.m.