Triple
T1515343
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tenerife |
E32105
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Adeje |
E173112
|
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: Adeje | Statement: [Tenerife, hasCity, Adeje]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Adeje Context triple: [Tenerife, hasCity, Adeje]
-
A.
Adeje
chosen
Adeje is a coastal municipality and popular tourist destination in the southwest of Tenerife in Spain’s Canary Islands.
-
B.
Anaga
Anaga is a rugged, mountainous district in northeastern Tenerife known for its ancient laurel forests, dramatic coastal landscapes, and traditional rural villages.
-
C.
Tafuna
Tafuna is a major suburban and commercial area on the island of Tutuila in American Samoa, known for its population density and proximity to the territory’s main airport.
-
D.
Paia
Paia is a small, laid-back town on Maui’s north shore known for its surf culture, bohemian vibe, and as a gateway to the Road to Hana.
-
E.
Kihei
Kihei is a coastal town on the southwest shore of Maui, Hawaii, known for its sunny weather, beaches, and resort and residential communities.
- 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_69a885e8caf88190a5fbb6159ce87786 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a907da75388190bfbdbedbd46adbdc |
completed | March 5, 2026, 4:34 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad294b16e481908a0b3cf7fd774caa |
completed | March 8, 2026, 7:46 a.m. |
Created at: March 4, 2026, 7:26 p.m.