Triple
T22103580
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mademoiselle Lanoire |
E546228
|
entity |
| Predicate | originalLanguageWork |
P74798
|
FINISHED |
| Object | French |
—
|
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 | Statement: [Mademoiselle Lanoire, originalLanguageWork, French]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: originalLanguageWork Context triple: [Mademoiselle Lanoire, originalLanguageWork, French]
-
A.
originalLanguageOfWholeWork
chosen
Indicates that a given language is the primary or original language in which an entire work (such as a book, film, or other complete creation) was first produced or expressed.
-
B.
originalLanguageOfCharacterWork
Indicates that a language is the original language in which a particular character-related work (e.g., story, script, or media) was created or first expressed.
-
C.
originalLanguageOfWinningWorks
Indicates the language in which the works that won an award or competition were originally created or written.
-
D.
originalLanguageAuthor
Indicates that an author created a work in a particular original language.
-
E.
originalLanguageText
Indicates that a text is expressed in its original, untranslated language.
- 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_69e11e378dc08190896d6a51597afd5a |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f129175a7881909549883f23c53dca |
completed | April 28, 2026, 9:39 p.m. |
| PD | Predicate disambiguation | batch_69e71b20ec50819096ac196c798f8e3c |
completed | April 21, 2026, 6:37 a.m. |
Created at: April 16, 2026, 8:30 p.m.