Triple
T2680122
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | French overseas territories |
E56552
|
entity |
| Predicate | haveOfficialLanguage |
P236
|
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: [French overseas territories, haveOfficialLanguage, French]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: haveOfficialLanguage Context triple: [French overseas territories, haveOfficialLanguage, French]
-
A.
hasLanguageOfOfficialName
Indicates that an entity’s official name is expressed in a specified language.
-
B.
officialLanguage
chosen
Indicates that a particular language has been formally designated by an authority as the official language used for government, legal, or administrative purposes in a given jurisdiction.
-
C.
additionalOfficialLanguage
Indicates that an entity has another language, beyond its primary one, that holds official or formally recognized status.
-
D.
isUNOfficialLanguage
Indicates that a language holds official status within the United Nations.
-
E.
previousOfficialLanguage
Indicates that one language formerly held official status in a country, region, or organization before being replaced or losing that status.
- 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_69ab4a4b13fc81909dfdb3f23da46832 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abda2f7bf88190a1e3103dd014d871 |
completed | March 7, 2026, 7:56 a.m. |
| PD | Predicate disambiguation | batch_69abd81ab9d08190b72b6104c6dbc769 |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:54 p.m.