Triple
T5572682
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Belu Regency |
E146239
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object | Atambua |
E531008
|
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: Atambua | Statement: [Belu Regency, hasSettlement, Atambua]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Atambua Context triple: [Belu Regency, hasSettlement, Atambua]
-
A.
Atambua
chosen
Atambua is a town in East Nusa Tenggara, Indonesia, located near the border with Timor-Leste and serving as an important regional trade and transit center.
-
B.
Nacala
Nacala is a coastal city in northern Mozambique known for its deep-water natural harbor and role as a major regional port and transport hub.
-
C.
Tabora
Tabora is a historic town in western Tanzania known as a regional trade center and former hub of 19th-century caravan routes.
-
D.
Vilankulo
Vilankulo is a coastal town in southern Mozambique known as the main gateway to the nearby Bazaruto Archipelago and its popular beach and marine tourism.
-
E.
Limbe
Limbe is a coastal city in southwestern Cameroon known for its black sand beaches, oil industry, and cultural diversity.
- 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_69c008ffed108190a084602227af6157 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c020518f348190879ac67dab307134 |
completed | March 22, 2026, 5:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c04d1c6c008190978682491cca1e84 |
completed | March 22, 2026, 8:12 p.m. |
Created at: March 22, 2026, 3:37 p.m.