Triple
T26955925
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | French Blue |
E678902
|
entity |
| Predicate | facetCount |
P78259
|
FINISHED |
| Object | approximately 112 facets |
—
|
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: approximately 112 facets | Statement: [French Blue, facetCount, approximately 112 facets]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: facetCount Context triple: [French Blue, facetCount, approximately 112 facets]
-
A.
hasFaceCountByType
Indicates the number of faces an entity has, broken down by specific face types or categories.
-
B.
numberOfFaces
chosen
Indicates the relationship that specifies how many faces a given object or entity has.
-
C.
faceTransitivity
Indicates that a facing or orientation relationship between entities is preserved or carried through transitively across intermediate entities.
-
D.
vertexCount
Indicates the number of vertices associated with a given geometric or graph-based entity.
-
E.
facesArea
Indicates that one entity is oriented toward, overlooks, or has its primary exposure directed toward a specified area.
- 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_69eeeb4e75f08190b14fc91ca4a91488 |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69f620e817a48190bdc81ec39833245a |
completed | May 2, 2026, 4:06 p.m. |
| PD | Predicate disambiguation | batch_69f611af72ac819094598dd2530d7411 |
completed | May 2, 2026, 3:01 p.m. |
Created at: April 27, 2026, 6:27 a.m.