Triple
T22321906
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sea of Sand |
E551806
|
entity |
| Predicate | nearestCity |
P350
|
FINISHED |
| Object | Probolinggo |
—
|
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: Probolinggo | Statement: [Sea of Sand, nearestCity, Probolinggo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Probolinggo Context triple: [Sea of Sand, nearestCity, Probolinggo]
-
A.
Probolinggo
chosen
Probolinggo is a coastal city in East Java, Indonesia, known as a common gateway for tourists visiting the Mount Bromo volcanic area.
-
B.
Ponorogo
Ponorogo is a regency-level town in Indonesia renowned as the cultural heartland of the Reog Ponorogo traditional dance and arts.
-
C.
Tulungagung
Tulungagung is a regency and urban center in southern East Java, Indonesia, known for its marble industry and coastal landscapes along the Indian Ocean.
-
D.
Purbalingga
Purbalingga is a regency and its capital town in Central Java, Indonesia, known for its manufacturing industries and proximity to mountainous tourist areas.
-
E.
Nganjuk
Nganjuk is a regency capital and regional urban center in the province of East Java, Indonesia.
- 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_69e11e4776588190abb21e5cea79973f |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15764d3a48190af79ce4642b7f563 |
completed | April 29, 2026, 12:57 a.m. |
Created at: April 16, 2026, 8:42 p.m.