Triple
T22134550
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Java |
E546991
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Bogor |
—
|
NE NERFINISHED |
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: Bogor | Statement: [Java, hasCity, Bogor]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bogor Context triple: [Java, hasCity, Bogor]
-
A.
Bogor
chosen
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.
-
B.
Cimahi
Cimahi is an urban city in Indonesia located near Bandung in the province of West Java, known historically as a military and training center.
-
C.
Tasikmalaya
Tasikmalaya is a significant city in West Java, Indonesia, known as an important cultural and economic hub for the Sundanese people.
-
D.
Bekasi
Bekasi is a large, rapidly growing industrial and residential city in the Greater Jakarta metropolitan area of Indonesia.
-
E.
Cilandak
Cilandak is a district in South Jakarta, Indonesia, known as a primarily residential and commercial area with several educational institutions and office complexes.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e39bf348190b541bfa16a7b71e0 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f129b8f4248190b6342c8d00942c25 |
completed | April 28, 2026, 9:42 p.m. |
Created at: April 16, 2026, 8:32 p.m.