Triple
T4699184
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lac de Serre-Ponçon |
E104223
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object |
Embrun
Embrun is a historic town in southeastern France’s Hautes-Alpes department, known for its picturesque setting in the Alps and proximity to the Lac de Serre-Ponçon.
|
E491876
|
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: Embrun | Statement: [Lac de Serre-Ponçon, locatedNear, Embrun]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Embrun Context triple: [Lac de Serre-Ponçon, locatedNear, Embrun]
-
A.
Embrun
Embrun is a rapidly growing Franco-Ontarian community in eastern Ontario, known for its bilingual character and proximity to Ottawa.
-
B.
Nyons
Nyons is a small town in southeastern France renowned for its olive production and picturesque setting in the Drôme Provençale region.
-
C.
Ambert
Ambert is a small commune in central France known for its traditional paper mills and as a center of production for Fourme d'Ambert blue cheese.
-
D.
Voiron
Voiron is a commune in southeastern France known for its historical town center and proximity to the Chartreuse Mountains.
-
E.
Valbonne
Valbonne is a picturesque village in southeastern France known for its preserved medieval old town and proximity to the technology hub of Sophia Antipolis.
- 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: Embrun Triple: [Lac de Serre-Ponçon, locatedNear, Embrun]
Generated description
Embrun is a historic town in southeastern France’s Hautes-Alpes department, known for its picturesque setting in the Alps and proximity to the Lac de Serre-Ponçon.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Embrun Target entity description: Embrun is a historic town in southeastern France’s Hautes-Alpes department, known for its picturesque setting in the Alps and proximity to the Lac de Serre-Ponçon.
-
A.
Embrun
Embrun is a rapidly growing Franco-Ontarian community in eastern Ontario, known for its bilingual character and proximity to Ottawa.
-
B.
Nyons
Nyons is a small town in southeastern France renowned for its olive production and picturesque setting in the Drôme Provençale region.
-
C.
Ambert
Ambert is a small commune in central France known for its traditional paper mills and as a center of production for Fourme d'Ambert blue cheese.
-
D.
Voiron
Voiron is a commune in southeastern France known for its historical town center and proximity to the Chartreuse Mountains.
-
E.
Valbonne
Valbonne is a picturesque village in southeastern France known for its preserved medieval old town and proximity to the technology hub of Sophia Antipolis.
- 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_69bd43e9b88481908582103dcadff3d9 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd63b57e1c8190962d97e4805974ed |
completed | March 20, 2026, 3:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69beb0c5ef848190a19eb01622ade42a |
completed | March 21, 2026, 2:52 p.m. |
| NEDg | Description generation | batch_69beb16170408190a04dded7fcc512d8 |
completed | March 21, 2026, 2:55 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69beb1c3bc5c8190b8a58baf2cd1ad44 |
completed | March 21, 2026, 2:57 p.m. |
Created at: March 20, 2026, 1:17 p.m.