Triple
T4474159
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bengkulu |
E98565
|
entity |
| Predicate | hasCapital |
P204
|
FINISHED |
| Object | Bengkulu City |
E98565
|
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: Bengkulu City | Statement: [Bengkulu, hasCapital, Bengkulu City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bengkulu City Context triple: [Bengkulu, hasCapital, Bengkulu City]
-
A.
Bengkulu
chosen
Bengkulu is a province on the southwest coast of the Indonesian island of Sumatra, known for its Indian Ocean shoreline and colonial history.
-
B.
Banjarmasin
Banjarmasin is a major riverine city in South Kalimantan, Indonesia, known for its historic floating markets and strategic location on the island of Borneo.
-
C.
Padang Besar
Padang Besar is a border town in northern Malaysia known as a key land gateway and trading hub between Malaysia and Thailand.
-
D.
Pekanbaru
Pekanbaru is a major commercial and transportation hub in central Sumatra, Indonesia, known for its oil industry and rapid urban growth.
-
E.
Binjai
Binjai is a city in Indonesia located near Medan on the island of Sumatra, known as a regional trade and transit hub.
- 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_69b3454b4ae481908967426dd37284d6 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b356bb03f48190a2addcd49c9e470d |
completed | March 13, 2026, 12:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b6513446f08190b4ab18dffda9060a |
completed | March 15, 2026, 6:27 a.m. |
Created at: March 12, 2026, 11:35 p.m.