Triple
T16274757
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Houyet |
E395094
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Rochefort |
E508274
|
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: Rochefort | Statement: [Houyet, borderedBy, Rochefort]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rochefort Context triple: [Houyet, borderedBy, Rochefort]
-
A.
Rochefort
Rochefort is a historic French port town on the Atlantic coast known for its naval heritage and maritime museum sites.
-
B.
Rochefort
chosen
Rochefort is a town in the Walloon region of Belgium, known for its historic abbey and Trappist beer.
-
C.
Rochefort
Rochefort is a municipality in the canton of Neuchâtel in western Switzerland.
-
D.
Rochefort-en-Terre
Rochefort-en-Terre is a picturesque medieval village in Brittany, France, renowned for its well-preserved stone architecture and status as one of the country’s most beautiful villages.
-
E.
Niort
Niort is a historic city in western France known as an administrative and economic center, particularly for its strong mutual insurance and financial services sector.
- 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_69d87f221d8081909b0b2063e7528ba2 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e2460c2d948190813b66e539a64a70 |
completed | April 17, 2026, 2:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a001f90d4088190915d701978f018a5 |
completed | May 10, 2026, 6:02 a.m. |
Created at: April 10, 2026, 5:05 a.m.