Triple
T17452934
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ECoC 2016 |
E424957
|
entity |
| Predicate | hasOfficialLanguageOfProgrammeCity |
P96074
|
FINISHED |
| Object | Spanish |
—
|
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: Spanish | Statement: [ECoC 2016, hasOfficialLanguageOfProgrammeCity, Spanish]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOfficialLanguageOfProgrammeCity Context triple: [ECoC 2016, hasOfficialLanguageOfProgrammeCity, Spanish]
-
A.
hasOfficialLanguageOfLocation
chosen
Indicates that a location has a specified language recognized as its official language.
-
B.
hasOfficialLanguageAtVenue
Indicates that a specific language is officially designated for use at a particular venue or location.
-
C.
hasOfficialCountryLanguage
Indicates that a country recognizes a particular language as one of its official languages for governmental or legal purposes.
-
D.
hasOfficialLanguageOfSurroundingCountry
Indicates that an entity uses as its official language the same language that is official in the country surrounding it.
-
E.
hasOfficialLanguageOfWork
Indicates that an entity uses a specified language as its official medium for conducting work or formal activities.
- 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_69d889db0ba481908402409af3b37917 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4513faa0c8190961cf504c459bf34 |
completed | April 19, 2026, 3:51 a.m. |
| PD | Predicate disambiguation | batch_69e3b4f0e3fc819094e466b74622c956 |
completed | April 18, 2026, 4:44 p.m. |
Created at: April 10, 2026, 5:47 a.m.