Triple
T36205849
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grand Huntsman |
E1047394
|
entity |
| Predicate | correspondingFrenchTitle |
P7000
|
FINISHED |
| Object | Grand Veneur |
—
|
NE NERFINISHED |
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: Grand Veneur | Statement: [Grand Huntsman, correspondingFrenchTitle, Grand Veneur]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: correspondingFrenchTitle Context triple: [Grand Huntsman, correspondingFrenchTitle, Grand Veneur]
-
A.
equivalentTitleInFrench
chosen
Indicates that one entity’s title is the equivalent or corresponding title of another entity, specifically expressed in French.
-
B.
equivalentTitleInLanguage
Indicates that two titles are equivalent in meaning or reference, but expressed in a specified language.
-
C.
titleInLanguage
Indicates that a specific title or name is expressed in a particular language.
-
D.
titleInEnglish
Indicates that an entity’s title or name is given in the English language.
-
E.
equivalentTitleInJapanese
Indicates that one entity has a corresponding or matching title in Japanese that is equivalent in meaning or usage to the other entity’s title.
- 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_69f76e414bdc8190996f15a544220a3d |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f9fd6834cc8190aa27153d6a99f3bb |
completed | May 5, 2026, 2:23 p.m. |
| PD | Predicate disambiguation | batch_69f7cf769338819092a5f42653dcc956 |
completed | May 3, 2026, 10:43 p.m. |
Created at: May 3, 2026, 4:08 p.m.