Triple
T15867237
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Errenteria |
E384743
|
entity |
| Predicate | isInLanguageRegion |
P96310
|
FINISHED |
| Object | Basque-speaking area |
—
|
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: Basque-speaking area | Statement: [Errenteria, isInLanguageRegion, Basque-speaking area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isInLanguageRegion Context triple: [Errenteria, isInLanguageRegion, Basque-speaking area]
-
A.
alsoInLanguageRegion
Indicates that two or more entities are located within or associated with the same language-defined geographic region.
-
B.
hasLanguageInCountry
Indicates that a particular language is used or recognized within a specified country.
-
C.
recognizedRegionalLanguage
Indicates that a language holds officially recognized status within a specific region or subnational jurisdiction.
-
D.
hasTraditionalLanguageRegion
chosen
Indicates the geographic region traditionally associated with the use or origin of a particular language.
-
E.
isInEnglishSpeakingCountry
Indicates that an entity is located in, associated with, or belongs to a country where English is an official or primary language of communication.
- 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_69d86da4e86481909f1325fdc971b5ec |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e174de2cd48190ab18e48c9f051a2a |
completed | April 16, 2026, 11:46 p.m. |
| PD | Predicate disambiguation | batch_69e142b976c081908d3ba3e705419f3a |
completed | April 16, 2026, 8:12 p.m. |
Created at: April 10, 2026, 4:50 a.m.