Triple
T20616304
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gaumais |
E506576
|
entity |
| Predicate | spokenIn |
P2266
|
FINISHED |
| Object | Gaume |
—
|
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: Gaume | Statement: [Gaumais, spokenIn, Gaume]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gaume Context triple: [Gaumais, spokenIn, Gaume]
-
A.
Gaume
chosen
Gaume is a culturally distinct region in southern Belgium known for its milder microclimate, French-speaking population, and characteristic rural landscapes.
-
B.
Dagneux
Dagneux is a commune in eastern France’s Ain department, known for its residential character and proximity to the Lyon metropolitan area.
-
C.
Baugnez
Baugnez is a small hamlet in eastern Belgium best known as the site of the World War II Malmedy massacre during the Battle of the Bulge.
-
D.
Gavisse
Gavisse is a small commune in northeastern France, located in the Moselle department near the border with Luxembourg and Germany.
-
E.
Garmes
Garmes is a surname most notably associated with Lee Garmes, an influential American cinematographer of Hollywood’s classic era.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4bc90988190ac360aaf645efc1d |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6aadbb3ac81908145f57fd6a94256 |
completed | April 20, 2026, 10:38 p.m. |
Created at: April 16, 2026, 11:41 a.m.