Triple
T16471191
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Calw |
E400063
|
entity |
| Predicate | twinTown |
P1072
|
FINISHED |
| Object | Aubenas |
E304556
|
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: Aubenas | Statement: [Calw, twinTown, Aubenas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aubenas Context triple: [Calw, twinTown, Aubenas]
-
A.
Aubenas
chosen
Aubenas is a historic market town in southern France known for its medieval castle and role as a commercial center of the Ardèche region.
-
B.
Gouvy
Gouvy is a rural municipality in the Belgian province of Luxembourg, known for its natural landscapes and proximity to the Ardennes region.
-
C.
Baugnez
Baugnez is a small hamlet in eastern Belgium best known as the site of the World War II Malmedy massacre during the Battle of the Bulge.
-
D.
Vauvert
Vauvert is a commune in southern France known for its location in the Gard department near the Camargue region.
-
E.
Bannalec
Bannalec is a commune in the Finistère department of Brittany in northwestern France, known for its rural character and Breton cultural heritage.
- 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_69d87f2dac988190b74d6e185fa88ba4 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e32dd0d2fc81909b68b5afb00f192f |
completed | April 18, 2026, 7:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00679ecf4c819096e7f698b81fe25a |
completed | May 10, 2026, 11:10 a.m. |
Created at: April 10, 2026, 5:11 a.m.