Triple
T6806726
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sisteron |
E156323
|
entity |
| Predicate | nearbyMajorCity |
P1982
|
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: [Sisteron, nearbyMajorCity, Gap]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gap Context triple: [Sisteron, nearbyMajorCity, Gap]
-
A.
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.
-
B.
Gap
Gap is a major American clothing and accessories retailer known for its casual, minimalist style and global high-street presence.
-
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 the commonly used abbreviation for Turkey’s Southeastern Anatolia Region, a largely rural area known for major dam and irrigation projects on the Euphrates and Tigris rivers.
- 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_69c68826e6a48190a3d220b541e639de |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d30a006081908996e31aa7ced0ac |
completed | March 27, 2026, 6:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c71aa27cec81909f45911ffa44ea6f |
completed | March 28, 2026, 12:02 a.m. |
Created at: March 27, 2026, 2:16 p.m.