Triple
T31364311
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Danescourt |
E799961
|
entity |
| Predicate | hasLocalLanguageContext |
P145951
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [Danescourt, hasLocalLanguageContext, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLocalLanguageContext Context triple: [Danescourt, hasLocalLanguageContext, English]
-
A.
hasLanguageContext
Indicates that an entity is associated with or interpreted within a specific language or linguistic context.
-
B.
hasLocalContext
Indicates that something exists or occurs within a specific, surrounding situational or environmental context tied to a particular place or scope.
-
C.
hasLanguageRegionContext
chosen
Indicates that something is associated with or situated within a specific linguistic or language-region context.
-
D.
hasLocalServicesLanguage
Indicates that the local services available in a given context operate or are provided using a specified language.
-
E.
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.
- 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_69f224e6b7448190ac6bf97ad7364160 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69fd783fed9c81909e792702636c4f1f |
completed | May 8, 2026, 5:44 a.m. |
| PD | Predicate disambiguation | batch_69fd7788e63c81909de22fdafcfe41c0 |
completed | May 8, 2026, 5:41 a.m. |
Created at: April 29, 2026, 9:18 p.m.