Triple
T26175679
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Şükür |
E654532
|
entity |
| Predicate | hasNotableLanguageContext |
P8383
|
FINISHED |
| Object | Turkish football |
—
|
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: Turkish football | Statement: [Şükür, hasNotableLanguageContext, Turkish football]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableLanguageContext Context triple: [Şükür, hasNotableLanguageContext, Turkish football]
-
A.
hasLanguageContext
chosen
Indicates that an entity is associated with or interpreted within a specific language or linguistic context.
-
B.
hasSignificantLanguage
Indicates that an entity possesses a language that plays an important or primary role in its communication, identity, or functioning.
-
C.
hasLanguageRegionContext
Indicates that something is associated with or situated within a specific linguistic or language-region context.
-
D.
hasLanguagePolicyContext
Indicates that there is an associated language-related policy, rule, or regulatory context governing how language is used or managed in relation to the subject.
-
E.
hasScripturalLanguageContext
Indicates that something is associated with, derived from, or interpreted within the linguistic and cultural context of scriptural or sacred texts.
- 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_69ee5b45873c81909499203612d05d07 |
completed | April 26, 2026, 6:36 p.m. |
| NER | Named-entity recognition | batch_69fcf1b3d9a08190850b388308656266 |
completed | May 7, 2026, 8:10 p.m. |
| PD | Predicate disambiguation | batch_69fcf0226d8c8190b23dceafb1794995 |
completed | May 7, 2026, 8:03 p.m. |
Created at: April 26, 2026, 8:37 p.m.