Triple
T3795184
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gard |
E89752
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Aigues-Mortes |
E109518
|
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: Aigues-Mortes | Statement: [Gard, contains, Aigues-Mortes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aigues-Mortes Context triple: [Gard, contains, Aigues-Mortes]
-
A.
Aigues-Mortes
chosen
Aigues-Mortes is a historic fortified town in southern France, renowned for its well-preserved medieval walls and proximity to the salt marshes of the Camargue.
-
B.
Nîmes
Nîmes is a historic city in southern France renowned for its well-preserved Roman monuments, including the Arena of Nîmes and the Maison Carrée.
-
C.
Frontignan
Frontignan is a coastal commune in southern France known for its Muscat wine production and Mediterranean setting near Sète.
-
D.
Villefranche-sur-Mer
Villefranche-sur-Mer is a picturesque coastal town in southeastern France known for its deep natural harbor, colorful old town, and scenic setting on the Mediterranean Sea.
-
E.
Argelès-sur-Mer
Argelès-sur-Mer is a coastal resort town in southern France known for its long Mediterranean beaches and proximity to the Pyrenees.
- 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_69aed9597d6881909b6ee3b9de859223 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aee79db2e88190b3aa2b8e8d885e19 |
completed | March 9, 2026, 3:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bde03b1f508190b9d5026103d3ee79 |
completed | March 21, 2026, 12:03 a.m. |
Created at: March 9, 2026, 3:15 p.m.