Triple
T31222829
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Graves cru classé (for red wine) |
E796060
|
entity |
| Predicate | hasNumberOfClassedEstates |
P34583
|
FINISHED |
| Object | 16 |
—
|
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: 16 | Statement: [Graves cru classé (for red wine), hasNumberOfClassedEstates, 16]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfClassedEstates Context triple: [Graves cru classé (for red wine), hasNumberOfClassedEstates, 16]
-
A.
numberOfClassifiedEstates
chosen
Indicates the count of estates that have been formally classified or categorized in a given context.
-
B.
numberOfEstates
Indicates the total count of estates associated with a given entity or context.
-
C.
hasEstateType
Indicates that an entity possesses or is associated with a particular category or type of estate.
-
D.
hadEstates
Indicates that an entity possessed or owned one or more estates (properties or landholdings).
-
E.
originalNumberOfEstates
Indicates the total count of estates that existed initially, before any changes or subsequent events.
- 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_69f224da98f88190ab32f690cce5d303 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69fd9ff026a48190bfec33deeb3b2c43 |
completed | May 8, 2026, 8:33 a.m. |
| PD | Predicate disambiguation | batch_69fd97d805bc8190ba12f429d3ad04c7 |
completed | May 8, 2026, 7:59 a.m. |
Created at: April 29, 2026, 9:10 p.m.