Triple
T5508323
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Palma de Mallorca |
E144499
|
entity |
| Predicate | localName |
P657
|
FINISHED |
| Object | Palma |
E144499
|
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: Palma | Statement: [Palma de Mallorca, localName, Palma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Palma Context triple: [Palma de Mallorca, localName, Palma]
-
A.
Palma
Palma is a Spanish-origin surname borne by various notable individuals across the Spanish-speaking world and beyond.
-
B.
Palma de Mallorca
chosen
Palma de Mallorca is the historic coastal city and major tourist destination that serves as the political, cultural, and economic center of Spain’s Balearic Islands.
-
C.
Mahón
Mahón is the principal city and administrative center of the Spanish Balearic island of Menorca, known for its large natural harbor and historic architecture.
-
D.
Denia
Denia is a coastal city on Spain’s Costa Blanca known for its historic castle, Mediterranean beaches, and vibrant port.
-
E.
Benidorm
Benidorm is a major Spanish Mediterranean resort city famous for its skyscraper-lined beaches, vibrant nightlife, and mass tourism.
- 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_69c008f6b5048190a09064116062cf69 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01f4a80d88190bab0056c4c78be93 |
completed | March 22, 2026, 4:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c027c082108190a3c8f826a6aeef4c |
completed | March 22, 2026, 5:32 p.m. |
Created at: March 22, 2026, 3:33 p.m.