Triple
T4014608
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gard |
E90724
|
entity |
| Predicate | containsPartOf |
P1393
|
FINISHED |
| Object |
Camargue
Camargue is a vast wetland region in southern France known for its wild white horses, pink flamingos, and salt marshes.
|
E10340
|
NE FINISHED |
How this triple was built (4 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: Camargue | Statement: [Gard, containsPartOf, Camargue]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Camargue Context triple: [Gard, containsPartOf, Camargue]
-
A.
Camargue
Camargue is a vast wetland region in southern France known for its salt marshes, wild white horses, black bulls, and rich birdlife including flamingos.
-
B.
Port Camargue
Port Camargue is a large Mediterranean marina in southern France, known as one of Europe’s biggest pleasure-boat harbors and a major center for nautical tourism.
-
C.
Dombes
Dombes is a historic rural region in eastern France known for its many ponds, wetlands, and traditional fish farming.
-
D.
Ver-sur-Mer
Ver-sur-Mer is a coastal village in Normandy, France, known for its location on Gold Beach, one of the key Allied landing sectors during the D-Day invasion of World War II.
-
E.
Entre-Deux-Mers
Entre-Deux-Mers is a wine-producing subregion of Bordeaux in southwestern France, known primarily for its dry white wines.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Camargue Triple: [Gard, containsPartOf, Camargue]
Generated description
Camargue is a vast wetland region in southern France known for its wild white horses, pink flamingos, and salt marshes.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Camargue Target entity description: Camargue is a vast wetland region in southern France known for its wild white horses, pink flamingos, and salt marshes.
-
A.
Camargue
chosen
Camargue is a vast wetland region in southern France known for its salt marshes, wild white horses, black bulls, and rich birdlife including flamingos.
-
B.
Port Camargue
Port Camargue is a large Mediterranean marina in southern France, known as one of Europe’s biggest pleasure-boat harbors and a major center for nautical tourism.
-
C.
Dombes
Dombes is a historic rural region in eastern France known for its many ponds, wetlands, and traditional fish farming.
-
D.
Ver-sur-Mer
Ver-sur-Mer is a coastal village in Normandy, France, known for its location on Gold Beach, one of the key Allied landing sectors during the D-Day invasion of World War II.
-
E.
Entre-Deux-Mers
Entre-Deux-Mers is a wine-producing subregion of Bordeaux in southwestern France, known primarily for its dry white wines.
- F. None of above.
Provenance (5 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_69aed95e44088190aff7d90a151b1b20 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefaa5afdc8190b709af2473d75d02 |
completed | March 9, 2026, 4:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b556295aa081908e803233b986fec9 |
completed | March 14, 2026, 12:35 p.m. |
| NEDg | Description generation | batch_69b556dd4c4c819082c08f0278f174a3 |
completed | March 14, 2026, 12:38 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b557e929088190878c9af98207f64a |
completed | March 14, 2026, 12:43 p.m. |
Created at: March 9, 2026, 3:35 p.m.