Triple
T11211767
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ordino |
E265325
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object |
La Massana
La Massana is a parish in northwestern Andorra known for its mountainous landscape, ski resorts, and outdoor tourism.
|
E937915
|
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: La Massana | Statement: [Ordino, near, La Massana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: La Massana Context triple: [Ordino, near, La Massana]
-
A.
Figaró-Montmany
Figaró-Montmany is a small municipality in the province of Barcelona, Catalonia, Spain, situated in a mountainous area near the Montseny Natural Park.
-
B.
Arenys de Munt
Arenys de Munt is a municipality in the Maresme comarca of Catalonia, Spain, known for its Mediterranean setting and involvement in early Catalan independence referendums.
-
C.
Segarra
Segarra is a historical inland comarca in Catalonia, Spain, known for its rolling cereal plains, medieval castles, and the town of Cervera as its capital.
-
D.
Garraf
Garraf is a coastal comarca in Catalonia, Spain, known for its Mediterranean landscapes, natural park, and seaside towns such as Sitges and Vilanova i la Geltrú.
-
E.
Manresa
Manresa is a historic city in Catalonia, Spain, known for its medieval architecture and significance as a religious and commercial center in the region.
- 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: La Massana Triple: [Ordino, near, La Massana]
Generated description
La Massana is a parish in northwestern Andorra known for its mountainous landscape, ski resorts, and outdoor tourism.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: La Massana Target entity description: La Massana is a parish in northwestern Andorra known for its mountainous landscape, ski resorts, and outdoor tourism.
-
A.
Figaró-Montmany
Figaró-Montmany is a small municipality in the province of Barcelona, Catalonia, Spain, situated in a mountainous area near the Montseny Natural Park.
-
B.
Arenys de Munt
Arenys de Munt is a municipality in the Maresme comarca of Catalonia, Spain, known for its Mediterranean setting and involvement in early Catalan independence referendums.
-
C.
Segarra
Segarra is a historical inland comarca in Catalonia, Spain, known for its rolling cereal plains, medieval castles, and the town of Cervera as its capital.
-
D.
Garraf
Garraf is a coastal comarca in Catalonia, Spain, known for its Mediterranean landscapes, natural park, and seaside towns such as Sitges and Vilanova i la Geltrú.
-
E.
Manresa
Manresa is a historic city in Catalonia, Spain, known for its medieval architecture and significance as a religious and commercial center in the region.
- 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_69d6aac59460819089b9848b27f57848 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8d6f5d4819086dcb776a0d469e8 |
completed | April 9, 2026, 5:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ee861f89b48190b06fba51475497e6 |
completed | April 26, 2026, 9:39 p.m. |
| NEDg | Description generation | batch_69eeb310e04c8190a1004662d5bbc015 |
completed | April 27, 2026, 12:51 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69eee95dfff48190a3c3022cdfc6dafc |
completed | April 27, 2026, 4:43 a.m. |
Created at: April 8, 2026, 9:30 p.m.