Triple
T38243517
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pont Samson |
E1013831
|
entity |
| Predicate | langueOfficielleDuToponyme |
P189481
|
FINISHED |
| Object | français |
—
|
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: français | Statement: [Pont Samson, langueOfficielleDuToponyme, français]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: langueOfficielleDuToponyme Context triple: [Pont Samson, langueOfficielleDuToponyme, français]
-
A.
hasOfficialLanguageOfToponym
chosen
Indicates that a toponym is associated with an official language in which that place name is formally recognized or used.
-
B.
hasOfficialFrenchName
Indicates that an entity possesses an officially recognized name in the French language.
-
C.
hasLanguageOfToponym
Indicates that a place name (toponym) is expressed in or associated with a particular language.
-
D.
isFrancophoneCounterpartOf
Indicates that one entity serves as the French-speaking or French-language equivalent or counterpart of another entity.
-
E.
languageVariantNameInFrench
Indicates the French-language name used to refer to a particular language variant.
- 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_69f76dd7e89c8190b7866bc85aea521b |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a0204dafcc08190868a6f3eab162505 |
completed | May 11, 2026, 4:33 p.m. |
| PD | Predicate disambiguation | batch_6a0202dc60348190a140bb9da906f5b6 |
completed | May 11, 2026, 4:25 p.m. |
Created at: May 3, 2026, 4:30 p.m.