Triple
T13272377
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mamuju |
E316096
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Mamuju Regency |
E316096
|
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: Mamuju Regency | Statement: [Mamuju, partOf, Mamuju Regency]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mamuju Regency Context triple: [Mamuju, partOf, Mamuju Regency]
-
A.
Mamuju
chosen
Mamuju is a coastal city on the island of Sulawesi in Indonesia known as an administrative and economic center in the region.
-
B.
Cheongdo County
Cheongdo County is a rural administrative region in southeastern South Korea known for its traditional culture, agricultural products, and annual bullfighting festival.
-
C.
Buan County
Buan County is a coastal administrative region in North Jeolla Province, South Korea, known for its scenic national parks, tidal flats, and cultural heritage sites.
-
D.
Hojai
Hojai is a town in the Indian state of Assam known as a commercial and cultural center, particularly for its role in the region’s trade and local industries.
-
E.
Gijang County
Gijang County is a coastal administrative region in northeastern Busan, South Korea, known for its scenic shoreline, seafood, and growing residential and tourist areas.
- 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_69d806b1d9ac8190852c5571d5bd5f0f |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99020f710819094c2618662bdc7fd |
completed | April 11, 2026, 12:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f71f1c2bac81909ac13624f7972919 |
completed | May 3, 2026, 10:10 a.m. |
Created at: April 9, 2026, 9:26 p.m.