Triple
T35886885
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Déesse |
E1037671
|
entity |
| Predicate | associatedWithNotableUse |
P5773
|
FINISHED |
| Object | official car of French presidents |
—
|
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: official car of French presidents | Statement: [La Déesse, associatedWithNotableUse, official car of French presidents]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithNotableUse Context triple: [La Déesse, associatedWithNotableUse, official car of French presidents]
-
A.
notableUse
chosen
Indicates that something is prominently or famously used by a particular entity, context, or for a specific purpose.
-
B.
associatedWithUse
Indicates a relationship where one entity is connected to or involved in the use or utilization of another entity.
-
C.
notablyAssociatedWith
Indicates that one entity is prominently or distinctively connected with another in a way that is especially noteworthy or remarkable.
-
D.
notableUseEvent
Indicates an event in which something is used in a way that is significant or noteworthy, distinguishing it from ordinary or routine use.
-
E.
notableUseCountry
Indicates that something is notably or prominently used within a particular country.
- 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_69f76e1f4d748190bb55594d8441d70e |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a037ce70f54819082946dad8d380825 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a069e6c8190857b611fffb7b867 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:06 p.m.