Triple
T9927064
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gare d'Enghien-les-Bains |
E187948
|
entity |
| Predicate | connectsTo |
P845
|
FINISHED |
| Object |
Persan-Beaumont
Persan-Beaumont is a commune in the Val-d'Oise department in the northern suburbs of Paris, France.
|
E829149
|
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: Persan-Beaumont | Statement: [Gare d'Enghien-les-Bains, connectsTo, Persan-Beaumont]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Persan-Beaumont Context triple: [Gare d'Enghien-les-Bains, connectsTo, Persan-Beaumont]
-
A.
Pontmain
Pontmain is a small village in northwestern France best known as the site of a 19th-century Marian apparition that led to its development as a Catholic pilgrimage destination.
-
B.
Moulinois
Moulinois is the French term for an inhabitant or native of the town of Moulins in central France.
-
C.
Le Bémont
Le Bémont is a small municipality in the Jura canton of Switzerland, situated on the Franches-Montagnes plateau and known for its rural, pastoral landscape.
-
D.
Butte-aux-Cailles
Butte-aux-Cailles is a picturesque, village-like neighborhood in Paris known for its cobbled streets, street art, and lively cafés.
-
E.
Plaisance
Plaisance is a commune in northern Haiti known for its mountainous terrain, rural communities, and agricultural activity.
- 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: Persan-Beaumont Triple: [Gare d'Enghien-les-Bains, connectsTo, Persan-Beaumont]
Generated description
Persan-Beaumont is a commune in the Val-d'Oise department in the northern suburbs of Paris, France.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Persan-Beaumont Target entity description: Persan-Beaumont is a commune in the Val-d'Oise department in the northern suburbs of Paris, France.
-
A.
Pontmain
Pontmain is a small village in northwestern France best known as the site of a 19th-century Marian apparition that led to its development as a Catholic pilgrimage destination.
-
B.
Moulinois
Moulinois is the French term for an inhabitant or native of the town of Moulins in central France.
-
C.
Le Bémont
Le Bémont is a small municipality in the Jura canton of Switzerland, situated on the Franches-Montagnes plateau and known for its rural, pastoral landscape.
-
D.
Butte-aux-Cailles
Butte-aux-Cailles is a picturesque, village-like neighborhood in Paris known for its cobbled streets, street art, and lively cafés.
-
E.
Plaisance
Plaisance is a commune in northern Haiti known for its mountainous terrain, rural communities, and agricultural activity.
- 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_69ca82b22a688190b52c75bd48429c10 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cdb59b85f88190899ea279fc02660f |
completed | April 2, 2026, 12:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d20e1eace88190a591cbab02153869 |
completed | April 5, 2026, 7:24 a.m. |
| NEDg | Description generation | batch_69d20ed2dea481909fd9a9dddac3daf1 |
completed | April 5, 2026, 7:27 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d20fef723881909d8d57548461926f |
completed | April 5, 2026, 7:31 a.m. |
Created at: March 30, 2026, 8:43 p.m.