Triple
T17629424
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Écrins National Park |
E429934
|
entity |
| Predicate | hasEntranceLocality |
P6140
|
FINISHED |
| Object | La Grave |
—
|
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: La Grave | Statement: [Écrins National Park, hasEntranceLocality, La Grave]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: La Grave Context triple: [Écrins National Park, hasEntranceLocality, La Grave]
-
A.
La Grave
chosen
La Grave is a high-altitude village and renowned off-piste ski and mountaineering destination in the French Alps.
-
B.
Gavignano
Gavignano is a small Italian town in the Lazio region, historically notable as the birthplace of Pope Innocent III.
-
C.
Carovigno
Carovigno is a historic town and popular tourist destination in Italy’s Apulia region, known for its medieval castle, olive groves, and proximity to the Adriatic coast.
-
D.
Corsico
Corsico is a municipality in the Metropolitan City of Milan in northern Italy, known as a residential and industrial suburb of the city.
-
E.
Carpiagne
Carpiagne is a French military camp and training area located near Marseille in southern France.
- 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_69d889e37f308190a6aa0a69daff86c7 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e46dbf59dc8190a56aa4a2449b2e2e |
completed | April 19, 2026, 5:53 a.m. |
Created at: April 10, 2026, 5:52 a.m.