Triple
T6483397
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nîmes |
E146449
|
entity |
| Predicate | isPrefectureOf |
P3877
|
FINISHED |
| Object | Gard |
E89752
|
NE 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: Gard | Statement: [Nîmes, isPrefectureOf, Gard]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gard Context triple: [Nîmes, isPrefectureOf, Gard]
-
A.
Gard
chosen
Gard is a department in southern France known for its Mediterranean landscapes, historic towns, and the famous Pont du Gard Roman aqueduct.
-
B.
Gardingen
Gardingen is a small locality in northern Germany best known as the birthplace of the renowned classical scholar and historian Theodor Mommsen.
-
C.
Gaten
Gaten is the first name of Gaten Matarazzo, an American actor best known for his role as Dustin Henderson on the Netflix series "Stranger Things."
-
D.
Orry
Orry is the given name of Orry-Kelly, the acclaimed Australian costume designer who won multiple Academy Awards for his work in Hollywood cinema.
-
E.
Gardein
Gardein is a plant-based food brand known for its wide range of meatless products such as chicken, beef, and fish alternatives made from soy, wheat, and pea proteins.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69c0090158c08190af0df9a2348d2d52 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c06a6cd0c4819085a921e6a361d91c |
completed | March 22, 2026, 10:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c65fd88d7c8190a98b7a48d49280c3 |
completed | March 27, 2026, 10:45 a.m. |
Created at: March 22, 2026, 4:52 p.m.