Triple
T18261767
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miss Saigon |
E437373
|
entity |
| Predicate | originalLanguageVersion |
P5477
|
FINISHED |
| Object | French concept |
—
|
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: French concept | Statement: [Miss Saigon, originalLanguageVersion, French concept]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: originalLanguageVersion Context triple: [Miss Saigon, originalLanguageVersion, French concept]
-
A.
originalLanguageText
Indicates that a text is expressed in its original, untranslated language.
-
B.
originalPublicationLanguageVariant
chosen
Indicates that one language is a specific variant or version of the language in which a work was originally published.
-
C.
originalLanguageContext
Indicates the language in which something was first created or expressed, providing the original linguistic context for its content or meaning.
-
D.
originalLanguageSupport
Indicates that one entity provides or maintains functionality, content, or interaction in the original language of another entity.
-
E.
originalTextLanguage
Indicates the language in which a text was originally written or created before any translation or adaptation.
- 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_69d8b913351c8190932b6a426de04b41 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4ff76a1208190abbe6ab8720ed154 |
completed | April 19, 2026, 4:14 p.m. |
| PD | Predicate disambiguation | batch_69e44fcdee748190bae6fb76e0cb22f3 |
completed | April 19, 2026, 3:45 a.m. |
Created at: April 10, 2026, 10:34 a.m.