Triple
T12983430
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seigneur de Bayard |
E321706
|
entity |
| Predicate | originalEpithetLanguage |
P58172
|
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: [Seigneur de Bayard, originalEpithetLanguage, French]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: originalEpithetLanguage Context triple: [Seigneur de Bayard, originalEpithetLanguage, French]
-
A.
originalNameLanguage
Indicates that the specified language is the language in which an entity’s original or primary name was expressed.
-
B.
languageOfEpithet
chosen
Indicates the language in which an epithet (such as a descriptive or honorary title) is expressed.
-
C.
eponymLanguage
Indicates that a language is named after (is the eponym of) a particular person, place, or entity.
-
D.
originalLanguagePhrase
Indicates that one phrase is the original-language version from which another phrase (typically a translation or adaptation) is derived.
-
E.
equivalentEpithetLanguage
Indicates that two epithets are expressed in different languages but convey the same meaning or designation.
- 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_69d8076479b8819090afce3591939cdf |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97f2a71a0819098bb6cf8a4b2208a |
completed | April 10, 2026, 10:52 p.m. |
| PD | Predicate disambiguation | batch_69d97dbdd94c8190ac4bbecca02dc77b |
completed | April 10, 2026, 10:46 p.m. |
Created at: April 9, 2026, 8:39 p.m.