Triple
T23336804
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sylvan, South Carolina |
E591612
|
entity |
| Predicate | languageOfWorkWhereFeatured |
P3681
|
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: [Sylvan, South Carolina, languageOfWorkWhereFeatured, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfWorkWhereFeatured Context triple: [Sylvan, South Carolina, languageOfWorkWhereFeatured, English]
-
A.
languageSpecifies
Indicates that one entity defines or constrains the syntax, semantics, or usage rules that govern how another language or linguistic system is expressed or interpreted.
-
B.
languageOfProduct
Indicates the language in which a product is written, labeled, presented, or otherwise made available.
-
C.
languageOfSurroundingCountry
Indicates that a language is the primary or commonly used language in the country surrounding a given place or region.
-
D.
presentedInLanguage
chosen
Indicates that something (such as content, information, or a work) is expressed or made available using a particular language.
-
E.
workLanguageVariant
Indicates that one language variant of a work is related to another version of the same work, typically differing by language or localization.
- 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_69e25d20156c81908c5c53195bd9c738 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f197f1e0588190bf073b92be0bf9e4 |
completed | April 29, 2026, 5:32 a.m. |
| PD | Predicate disambiguation | batch_69effcfd8d288190937a887fe6023c11 |
completed | April 28, 2026, 12:19 a.m. |
Created at: April 17, 2026, 5:17 p.m.