Triple
T8856287
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alicante region |
E210764
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Denia |
E425416
|
NE FINISHED |
How this triple was built (2 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: Denia | Statement: [Alicante region, contains, Denia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Denia Context triple: [Alicante region, contains, Denia]
-
A.
Denia
chosen
Denia is a coastal city on Spain’s Costa Blanca known for its historic castle, Mediterranean beaches, and vibrant port.
-
B.
Benidorm
Benidorm is a major Spanish Mediterranean resort city famous for its skyscraper-lined beaches, vibrant nightlife, and mass tourism.
-
C.
Deià
Deià is a picturesque coastal village on the Spanish island of Mallorca, famed for its dramatic mountain-and-sea scenery and its long association with artists and writers.
-
D.
Lloret de Mar
Lloret de Mar is a popular Mediterranean coastal resort town on Spain’s Costa Brava, known for its beaches, nightlife, and tourism.
-
E.
Pietrasanta
Pietrasanta is a historic Tuscan town in Italy renowned for its marble workshops, sculpture studios, and vibrant community of international artists.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ca838a424c8190b1ecac115c2927e7 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc60e259248190abd334bbfcdcb955 |
completed | April 1, 2026, 12:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfc1c3c6c08190b51ea5e9cf7085e9 |
completed | April 3, 2026, 1:33 p.m. |
Created at: March 30, 2026, 6:50 p.m.