Triple
T174226
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Indonesia |
E3541
|
entity |
| Predicate | majorCity |
P316
|
FINISHED |
| Object | Semarang |
E10696
|
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: Semarang | Statement: [Indonesia, majorCity, Semarang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Semarang Context triple: [Indonesia, majorCity, Semarang]
-
A.
Semarang
chosen
Semarang is a major coastal city on the north coast of Java in Indonesia, known historically as an important colonial trading hub and now as a significant commercial and industrial center.
-
B.
Surabaya
Surabaya is Indonesia’s second-largest city and a key commercial and industrial hub on the island of Java, historically serving as one of the region’s most important seaports.
-
C.
Bandung
Bandung is a large Indonesian city on the island of Java known for its cool climate, universities, colonial and art deco architecture, and role as a center of culture and technology.
-
D.
Banting
Banting is a surname most famously associated with Frederick Banting, the Canadian physician and Nobel laureate who co-discovered insulin.
-
E.
Medan
Medan is a major economic and cultural hub in northern Sumatra, known as one of Indonesia’s largest cities and a gateway to the region.
- 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_69a25374990081909766d30c79a18e0e |
completed | Feb. 28, 2026, 2:31 a.m. |
| NER | Named-entity recognition | batch_69a258e32da88190ad9485aecd0bf08f |
completed | Feb. 28, 2026, 2:54 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a2fd02709c819096db4f4c242d463f |
completed | Feb. 28, 2026, 2:34 p.m. |
Created at: Feb. 28, 2026, 2:39 a.m.