Triple
T13093922
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Port of Bitung |
E310531
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Bitung |
E316726
|
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: Bitung | Statement: [Port of Bitung, locatedIn, Bitung]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bitung Context triple: [Port of Bitung, locatedIn, Bitung]
-
A.
Bitung
chosen
Bitung is a port city in North Sulawesi, Indonesia, known as a major gateway for maritime trade and access to the rich marine biodiversity of the Lembeh Strait.
-
B.
Ambon
Ambon is a major city and port in eastern Indonesia, known as an administrative, economic, and cultural hub in the Maluku region.
-
C.
Manado
Manado is a major coastal city and the capital of North Sulawesi province in Indonesia, known as a gateway to the renowned Bunaken Marine Park.
-
D.
Tondano
Tondano is a town in North Sulawesi, Indonesia, known as an administrative and cultural center of the Minahasa region near Lake Tondano.
-
E.
Palu
Palu is a historic town and district in eastern Turkey known for its ancient ruins and location along the Murat River in Elazığ Province.
- 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_69d806a733548190989cfd4ce981ca33 |
completed | April 9, 2026, 8:05 p.m. |
| NER | Named-entity recognition | batch_69d9813cd1b881909871a318fdd60672 |
completed | April 10, 2026, 11:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f70a227d2c81908da0089d0e0387c6 |
completed | May 3, 2026, 8:41 a.m. |
Created at: April 9, 2026, 9:03 p.m.