Triple
T7102328
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bogor Regency |
E165487
|
entity |
| Predicate | hasBorderWith |
P224
|
FINISHED |
| Object | Depok |
E175029
|
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: Depok | Statement: [Bogor Regency, hasBorderWith, Depok]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Depok Context triple: [Bogor Regency, hasBorderWith, Depok]
-
A.
Depok
chosen
Depok is a rapidly growing commuter city in Indonesia located between Jakarta and Bogor, known for its universities and residential developments.
-
B.
Bekasi
Bekasi is a large, rapidly growing industrial and residential city in the Greater Jakarta metropolitan area of Indonesia.
-
C.
Bogor
Bogor is a city on the Indonesian island of Java known for its cool climate, botanical gardens, and role as a major educational and research center.
-
D.
Bogor Regency
Bogor Regency is an administrative region in West Java, Indonesia, that encircles the city of Bogor and is known for its rapidly growing suburban and rural communities.
-
E.
Tangerang
Tangerang is a major urban and industrial city in Indonesia located just west of Jakarta on the island of Java.
- 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_69c6887fcddc8190a5d58908f6dee590 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e588aee08190bfb3d96135c0a322 |
completed | March 27, 2026, 8:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7b8d23bc0819095b7a3bf09b8bacd |
completed | March 28, 2026, 11:17 a.m. |
Created at: March 27, 2026, 2:42 p.m.