Triple
T18573248
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Landes |
E453922
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Labenne
Labenne is a coastal commune in southwestern France, known for its Atlantic beaches and location in the Landes department of the Nouvelle-Aquitaine region.
|
E1331427
|
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: Labenne | Statement: [Landes, contains, Labenne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Labenne Context triple: [Landes, contains, Labenne]
-
A.
Pierrelatte
Pierrelatte is a commune in southeastern France known for its proximity to the Tricastin nuclear complex and its location in the Drôme department of the Auvergne-Rhône-Alpes region.
-
B.
Activia
Activia is a popular probiotic yogurt brand known for supporting digestive health, produced and marketed by Danone.
-
C.
Bessan
Bessan is a commune in southern France, known for its historic village center and wine-producing countryside in the Hérault department.
-
D.
Bouchercon
Bouchercon is an annual convention dedicated to mystery and crime fiction that brings together authors, fans, and industry professionals for panels, awards, and related events.
-
E.
Lardé
Lardé is the surname of Alicia Esther Lardé, a Salvadoran-born physicist and the first wife of mathematician John Nash.
- 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: Labenne Triple: [Landes, contains, Labenne]
Generated description
Labenne is a coastal commune in southwestern France, known for its Atlantic beaches and location in the Landes department of the Nouvelle-Aquitaine region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Labenne Target entity description: Labenne is a coastal commune in southwestern France, known for its Atlantic beaches and location in the Landes department of the Nouvelle-Aquitaine region.
-
A.
Pierrelatte
Pierrelatte is a commune in southeastern France known for its proximity to the Tricastin nuclear complex and its location in the Drôme department of the Auvergne-Rhône-Alpes region.
-
B.
Activia
Activia is a popular probiotic yogurt brand known for supporting digestive health, produced and marketed by Danone.
-
C.
Bessan
Bessan is a commune in southern France, known for its historic village center and wine-producing countryside in the Hérault department.
-
D.
Bouchercon
Bouchercon is an annual convention dedicated to mystery and crime fiction that brings together authors, fans, and industry professionals for panels, awards, and related events.
-
E.
Lardé
Lardé is the surname of Alicia Esther Lardé, a Salvadoran-born physicist and the first wife of mathematician John Nash.
- F. None of above. chosen
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_69d8d38974308190a9174430ef256b73 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e543c7f63c81909b5d5764ffd20234 |
completed | April 19, 2026, 9:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a04f886f2e88190bf01061cb793c0ab |
completed | May 13, 2026, 10:17 p.m. |
| NEDg | Description generation | batch_6a04f9511eb08190952be765862cd1ab |
completed | May 13, 2026, 10:21 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a04f9dd338881908b300bab7947f736 |
completed | May 13, 2026, 10:23 p.m. |
Created at: April 10, 2026, 11:43 a.m.