Triple
T24565222
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beauchamp–Feuillet notation |
E607766
|
entity |
| Predicate | languageOfOriginalTreatise |
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: [Beauchamp–Feuillet notation, languageOfOriginalTreatise, French]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfOriginalTreatise Context triple: [Beauchamp–Feuillet notation, languageOfOriginalTreatise, French]
-
A.
languageOfOriginalDescription
Indicates that something is expressed or documented in its initial or source language version.
-
B.
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.
-
C.
languageOfManuscript
Indicates the language in which a given manuscript is written.
-
D.
languageOfOriginalGrant
Indicates the language in which the original grant or granting document was written or issued.
-
E.
originalLanguageAuthor
Indicates that an author created a work in a particular original 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_69e2c4cc35a48190990b7571bc086df8 |
completed | April 17, 2026, 11:39 p.m. |
| NER | Named-entity recognition | batch_69f2a8f9d0f881909afc04537c32f76b |
completed | April 30, 2026, 12:57 a.m. |
| PD | Predicate disambiguation | batch_69f2a6b99e7c8190ba7e2dc8729a314a |
completed | April 30, 2026, 12:47 a.m. |
Created at: April 18, 2026, 2:28 a.m.