Triple
T12781870
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Villeblevin |
E305526
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Sens |
E225980
|
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: Sens | Statement: [Villeblevin, locatedNear, Sens]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sens Context triple: [Villeblevin, locatedNear, Sens]
-
A.
Sens
chosen
Sens is a historic commune in north-central France known for its impressive Gothic cathedral and role as a regional administrative and commercial center.
-
B.
Sinnes
Sinnes is a small village in Sirdal municipality in Agder county, Norway, known as a gateway to nearby mountain and ski areas.
-
C.
Senne
The Senne is a small river flowing through Brussels, Belgium, much of which has been covered over as the city developed.
-
D.
Sinn
Sinn is Gottlob Frege’s notion of “sense,” the mode of presentation through which a linguistic expression conveys its reference and cognitive significance.
-
E.
Sinn
Sinn is a river in northern Bavaria, Germany, that flows through the Lower Franconia region.
- 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_69d7bdf2b43c819098ae5aa68e61ea58 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96e5b52048190b279b7ad066efe9f |
completed | April 10, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f685030cbc8190856bf5254e231d25 |
completed | May 2, 2026, 11:13 p.m. |
Created at: April 9, 2026, 5:29 p.m.