Triple
T5770244
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Santa Eulària des Riu |
E127313
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object |
Es Canar
Es Canar is a small seaside resort village on the eastern coast of Ibiza, Spain, known for its beaches and popular weekly hippy market.
|
E544066
|
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: Es Canar | Statement: [Santa Eulària des Riu, hasSettlement, Es Canar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Es Canar Context triple: [Santa Eulària des Riu, hasSettlement, Es Canar]
-
A.
Tinja
Tinja is a small town in northern Tunisia known for its location near Lake Bizerte and its historical and ecological significance.
-
B.
Marín
Marín is a coastal town in the province of Pontevedra, Galicia, Spain, known for its naval traditions and as a base of the Spanish Navy.
-
C.
Molles
Molles is a small commune in central France, located in the Allier department within the Auvergne-Rhône-Alpes region.
-
D.
Alajeró
Alajeró is a small coastal and rural municipality on the island of La Gomera in Spain’s Canary Islands, known for its rugged landscapes and traditional Canarian character.
-
E.
Tasqueña
Tasqueña is a major transit hub and southern terminus of Mexico City’s Metro Line 2, integrating metro, light rail, and bus services.
- 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: Es Canar Triple: [Santa Eulària des Riu, hasSettlement, Es Canar]
Generated description
Es Canar is a small seaside resort village on the eastern coast of Ibiza, Spain, known for its beaches and popular weekly hippy market.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Es Canar Target entity description: Es Canar is a small seaside resort village on the eastern coast of Ibiza, Spain, known for its beaches and popular weekly hippy market.
-
A.
Tinja
Tinja is a small town in northern Tunisia known for its location near Lake Bizerte and its historical and ecological significance.
-
B.
Marín
Marín is a coastal town in the province of Pontevedra, Galicia, Spain, known for its naval traditions and as a base of the Spanish Navy.
-
C.
Molles
Molles is a small commune in central France, located in the Allier department within the Auvergne-Rhône-Alpes region.
-
D.
Alajeró
Alajeró is a small coastal and rural municipality on the island of La Gomera in Spain’s Canary Islands, known for its rugged landscapes and traditional Canarian character.
-
E.
Tasqueña
Tasqueña is a major transit hub and southern terminus of Mexico City’s Metro Line 2, integrating metro, light rail, and bus services.
- 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_69c00834f6308190851b0abeddd8ed7e |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c029aa877c8190bf6a944f18cca3b8 |
completed | March 22, 2026, 5:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c07e61127c8190833e279403af6605 |
completed | March 22, 2026, 11:42 p.m. |
| NEDg | Description generation | batch_69c08cc5c48481909c1ac21d586b3263 |
completed | March 23, 2026, 12:43 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c08d42cac88190b6cd454e8c31a4ef |
completed | March 23, 2026, 12:45 a.m. |
Created at: March 22, 2026, 3:50 p.m.