Triple
T7409584
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guanche language |
E170965
|
entity |
| Predicate | sourceOfWord |
P5801
|
FINISHED |
| Object | "Arona" |
E176074
|
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: "Arona" | Statement: [Guanche language, sourceOfWord, "Arona"]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: "Arona" Context triple: [Guanche language, sourceOfWord, "Arona"]
-
A.
Allerona
Allerona is a small historic hill town in the Umbria region of central Italy, known for its medieval architecture and scenic countryside.
-
B.
Arona
Arona is a town in northern Italy on the shores of Lake Maggiore, known for its historic architecture and as the birthplace of Saint Charles Borromeo.
-
C.
Arona
chosen
Arona is a coastal tourist municipality in southern Tenerife, Spain, known for its popular beach resorts such as Los Cristianos and Playa de las Américas.
-
D.
Arida
Arida is a city in Japan known for its agricultural production, particularly high-quality citrus fruits, within Wakayama Prefecture.
-
E.
Terêna
Terêna is an Arawakan language spoken by the Terena Indigenous people of Brazil, primarily in the state of Mato Grosso do Sul.
- 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_69c68a6010108190925e5284de022660 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f4eb5c808190ba08956bcf297ea8 |
completed | March 27, 2026, 9:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c811244df081909b63e085d2272cd2 |
completed | March 28, 2026, 5:34 p.m. |
Created at: March 27, 2026, 3:10 p.m.