Triple
T35027296
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chemin Vert station |
E1010374
|
entity |
| Predicate | networkLanguage |
P82602
|
FINISHED |
| Object | French |
—
|
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: French | Statement: [Chemin Vert station, networkLanguage, French]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: networkLanguage Context triple: [Chemin Vert station, networkLanguage, French]
-
A.
languageUse
Indicates the language or languages an entity uses for communication, expression, or interaction.
-
B.
navigationLanguage
chosen
Indicates the language used for navigation-related content, such as menus, directions, or interface controls.
-
C.
languageCategory
Indicates the classification relationship where a language is assigned to a particular linguistic or functional category.
-
D.
termLanguage
Indicates the language in which a given term is expressed or defined.
-
E.
languageUsedAs
Indicates that one language is employed in a specific role, function, or context relative to another entity or situation.
- 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_69f76dccf0108190af43b465d3750196 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f7858aa5508190a07dde993b3356fc |
completed | May 3, 2026, 5:27 p.m. |
| PD | Predicate disambiguation | batch_69f7841812f081909d878955d114088e |
completed | May 3, 2026, 5:21 p.m. |
Created at: May 3, 2026, 4:01 p.m.