Triple
T37627975
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cugy (FR) |
E936260
|
entity |
| Predicate | localAdministrationLanguage |
P141475
|
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: [Cugy (FR), localAdministrationLanguage, French]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: localAdministrationLanguage Context triple: [Cugy (FR), localAdministrationLanguage, French]
-
A.
languageOfCurrentAdministration
chosen
Indicates the language currently used by the governing or administrative authority in charge.
-
B.
languageOfTopLevelAdministration
Indicates the language officially used by the highest level of an administrative or governmental authority to conduct its functions and affairs.
-
C.
historicallyDominantLanguageOfAdministrationIn
Indicates that a language has historically been the primary language used for official governance and administrative functions within a given place or political entity.
-
D.
languageFamilyOfAdministration
Indicates the language family used as the primary medium of official governance or administrative functions for an entity.
-
E.
languageOfLocalOrganization
Indicates the language used or officially adopted by a local organization in its operations or communications.
- 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_69f76ed24820819081bafd36e9088701 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fccdd496048190bca801a8a9eecb62 |
completed | May 7, 2026, 5:37 p.m. |
| PD | Predicate disambiguation | batch_69fcccee6240819084680887731ff64b |
completed | May 7, 2026, 5:33 p.m. |
Created at: May 3, 2026, 4:18 p.m.