Triple
T18582594
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Fala de Xálima |
E454152
|
entity |
| Predicate | hasDialect |
P4251
|
FINISHED |
| Object | Lagarteiru |
—
|
NE NERFINISHED |
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: Lagarteiru | Statement: [A Fala de Xálima, hasDialect, Lagarteiru]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lagarteiru Context triple: [A Fala de Xálima, hasDialect, Lagarteiru]
-
A.
Lagarteiru
chosen
Lagarteiru is a regional dialect of the Fala language spoken in a small area of Extremadura in western Spain.
-
B.
Lagarto
Lagarto is a municipality in the Brazilian state of Sergipe, known for its agricultural activities and growing regional commerce.
-
C.
Tarántula
Tarántula is a spinning roller coaster at Madrid’s Parque de Atracciones known for its tight turns and disorienting, free-rotating cars.
-
D.
Grilo
Grilo is a character in Eça de Queirós’s novel "A Cidade e as Serras," serving as a practical and loyal servant who helps bridge the contrast between urban sophistication and rural simplicity.
-
E.
Pulga
Pulga is a small, scenic village in Himachal Pradesh, India, known for its tranquil Himalayan setting and popularity among trekkers and backpackers.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8d38ae7e081908a98df1251842402 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e543d10b1c8190a401df810b7290c9 |
completed | April 19, 2026, 9:06 p.m. |
Created at: April 10, 2026, 11:44 a.m.