Triple
T13230685
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Murten |
E315007
|
entity |
| Predicate | hasNeighboringMunicipality |
P224
|
FINISHED |
| Object |
Clavaleyres
Clavaleyres is a small Swiss municipality in the canton of Bern, known for its rural character and location near the town of Murten.
|
E1047427
|
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: Clavaleyres | Statement: [Murten, hasNeighboringMunicipality, Clavaleyres]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Clavaleyres Context triple: [Murten, hasNeighboringMunicipality, Clavaleyres]
-
A.
Vauvert
Vauvert is a commune in southern France known for its location in the Gard department near the Camargue region.
-
B.
Mouriès
Mouriès is a village in southern France’s Provence region, known for its olive oil production and location near the Alpilles hills.
-
C.
Roquebillière
Roquebillière is a small commune in southeastern France, situated in the Alpes-Maritimes department in the Provence-Alpes-Côte d’Azur region.
-
D.
Malaucène
Malaucène is a picturesque Provençal village in southeastern France, known as a popular base for cyclists and tourists visiting and climbing Mont Ventoux.
-
E.
Saussignac
Saussignac is a small wine-producing commune in southwestern France, known for its sweet white wines made primarily from Sémillon and other Bordeaux grape varieties.
- 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: Clavaleyres Triple: [Murten, hasNeighboringMunicipality, Clavaleyres]
Generated description
Clavaleyres is a small Swiss municipality in the canton of Bern, known for its rural character and location near the town of Murten.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Clavaleyres Target entity description: Clavaleyres is a small Swiss municipality in the canton of Bern, known for its rural character and location near the town of Murten.
-
A.
Vauvert
Vauvert is a commune in southern France known for its location in the Gard department near the Camargue region.
-
B.
Mouriès
Mouriès is a village in southern France’s Provence region, known for its olive oil production and location near the Alpilles hills.
-
C.
Roquebillière
Roquebillière is a small commune in southeastern France, situated in the Alpes-Maritimes department in the Provence-Alpes-Côte d’Azur region.
-
D.
Malaucène
Malaucène is a picturesque Provençal village in southeastern France, known as a popular base for cyclists and tourists visiting and climbing Mont Ventoux.
-
E.
Saussignac
Saussignac is a small wine-producing commune in southwestern France, known for its sweet white wines made primarily from Sémillon and other Bordeaux grape varieties.
- 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_69d806affc688190a25b6ccc588e9c72 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98d336ae08190bfc118cfbefddf84 |
completed | April 10, 2026, 11:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f75d7edb40819095dfa45d3c61a0e3 |
completed | May 3, 2026, 2:36 p.m. |
| NEDg | Description generation | batch_69f75fe31db08190b32d3d7c964f5354 |
completed | May 3, 2026, 2:46 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f76049056c8190b77f8ca78c8f77a0 |
completed | May 3, 2026, 2:48 p.m. |
Created at: April 9, 2026, 9:21 p.m.