Triple
T8751593
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Massa |
E207971
|
entity |
| Predicate | twinnedWith |
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: [Massa, twinnedWith, Aubenas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aubenas Context triple: [Massa, twinnedWith, 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.
Vauvert
Vauvert is a commune in southern France known for its location in the Gard department near the Camargue region.
-
D.
Maurepas
Maurepas is a commune in the Yvelines department in the Île-de-France region of north-central France, known as a residential suburb southwest of Paris.
-
E.
Vallauris
Vallauris is a town in the French Riviera renowned for its pottery tradition and its association with Pablo Picasso, who lived and worked there for several years.
- 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_69ca835bb2bc819084bb5906cb6ef7f8 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5da774f4819099e5bfd12973d946 |
completed | March 31, 2026, 11:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf518d48b481909e2abf5d60bf7c78 |
completed | April 3, 2026, 5:35 a.m. |
Created at: March 30, 2026, 6:39 p.m.