Triple
T13412905
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luwu Regency |
E320133
|
entity |
| Predicate | hasAdministrativeSeat |
P1474
|
FINISHED |
| Object | Belopa |
E1038895
|
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: Belopa | Statement: [Luwu Regency, hasAdministrativeSeat, Belopa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Belopa Context triple: [Luwu Regency, hasAdministrativeSeat, Belopa]
-
A.
Belopa
chosen
Belopa is a town in South Sulawesi, Indonesia, known as the administrative and economic center of Luwu Regency.
-
B.
Gilga
Gilga is a production company known for its work on the television series "Swarm."
-
C.
Baunei
Baunei is a coastal and mountain village in Sardinia, Italy, known for its dramatic limestone cliffs, hiking trails, and the famous Cala Goloritzé beach.
-
D.
Tambolaka
Tambolaka is a town on the Indonesian island of Sumba that serves as an important local hub with an airport and access point for exploring the island.
-
E.
Sapopemba
Sapopemba is a metro station on São Paulo’s Line 15–Silver monorail, serving the Sapopemba district in the city’s eastern zone.
- 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_69d806b943cc8190b6af624d385d7e12 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbaeb556948190af008c88e5bbf051 |
completed | April 12, 2026, 2:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f73987cc088190839e8a589086639c |
completed | May 3, 2026, 12:03 p.m. |
Created at: April 9, 2026, 9:35 p.m.