Triple
T7592205
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kuto-Kute dialect |
E179762
|
entity |
| Predicate | spokenIn |
P2266
|
FINISHED |
| Object | Lombok |
E20548
|
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: Lombok | Statement: [Kuto-Kute dialect, spokenIn, Lombok]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lombok Context triple: [Kuto-Kute dialect, spokenIn, Lombok]
-
A.
Lombok
chosen
Lombok is an Indonesian island east of Bali, known for its volcanic Mount Rinjani, beaches, and Sasak culture.
-
B.
Laworo
Laworo is a town in Indonesia that serves as one of the urban centers within the province of Southeast Sulawesi.
-
C.
Nambui
Nambui was a Mongol empress consort of the Yuan dynasty and a prominent wife of Kublai Khan, influential in the imperial court after the death of his first empress.
-
D.
Pakurumo
Pakurumo is a popular Afrobeat song by Nigerian artist Wizkid, known for its upbeat rhythm and dance-friendly vibe.
-
E.
Hatta
Hatta is an Indonesian surname most prominently associated with Mohammad Hatta, the country’s first vice president and a leading figure in the struggle for independence.
- 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_69c69f335248819093c1006f30513708 |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c6f9b92c348190b547f0aacfb8d6be |
completed | March 27, 2026, 9:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c86197fe0881908307a411cabdca7f |
completed | March 28, 2026, 11:17 p.m. |
Created at: March 27, 2026, 3:53 p.m.