Triple
T14091578
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saint-Bonnet-en-Champsaur |
E339143
|
entity |
| Predicate | locatedNorthOf |
P305
|
FINISHED |
| Object | Gap |
E104222
|
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: Gap | Statement: [Saint-Bonnet-en-Champsaur, locatedNorthOf, Gap]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gap Context triple: [Saint-Bonnet-en-Champsaur, locatedNorthOf, Gap]
-
A.
Gap
Gap is a major American clothing and accessories retailer known for its casual, minimalist style and global high-street presence.
-
B.
Gap
chosen
Gap is a town in southeastern France, known as the capital of the Hautes-Alpes department and a gateway to the French Alps.
-
C.
Deep Gap
Deep Gap is a mountain pass in the Appalachian region of North Carolina, commonly used as an access point for hiking routes such as the Deep Gap Trail.
-
D.
GAP
GAP is a Mexican airport operator that manages a network of major airports primarily along the Pacific coast and in western Mexico.
-
E.
GAP
GAP is a 150-mile rail-trail for hiking and biking that runs through Pennsylvania and Maryland, connecting Pittsburgh to Cumberland and linking with the C&O Canal Towpath to form a popular long-distance route to Washington, D.C.
- 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_69d81c687b0c819087fd9ed4198403f8 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de5ee3213c8190af2853a2a5b302a2 |
completed | April 14, 2026, 3:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcd0a7aab88190949cf1fd8e11b050 |
completed | May 7, 2026, 5:49 p.m. |
Created at: April 9, 2026, 10:21 p.m.