Triple
T31704058
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deuxième Cru Classé |
E809132
|
entity |
| Predicate | hasNumberOfEstatesCategory |
P63049
|
FINISHED |
| Object | several dozen châteaux |
—
|
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: several dozen châteaux | Statement: [Deuxième Cru Classé, hasNumberOfEstatesCategory, several dozen châteaux]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfEstatesCategory Context triple: [Deuxième Cru Classé, hasNumberOfEstatesCategory, several dozen châteaux]
-
A.
numberOfEstates
chosen
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.
hasEstateType
Indicates that an entity possesses or is associated with a particular category or type of estate.
-
D.
numberOfClassifiedEstates
Indicates the count of estates that have been formally classified or categorized in a given context.
-
E.
hasAssociatedEstate
Indicates that one entity is linked to, or has responsibility for, a particular estate or property.
- 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_69fddd373cdc8190be1b12e70e4deb1f |
completed | May 8, 2026, 12:55 p.m. |
| PD | Predicate disambiguation | batch_69fddc6915a88190ad41e379aa3ede13 |
completed | May 8, 2026, 12:51 p.m. |
Created at: April 30, 2026, 11:13 p.m.