Triple
T3677916
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mulhouse |
E78039
|
entity |
| Predicate | twinCity |
P1072
|
FINISHED |
| Object | Tangerang |
E185381
|
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: Tangerang | Statement: [Mulhouse, twinCity, Tangerang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tangerang Context triple: [Mulhouse, twinCity, Tangerang]
-
A.
Tangerang
chosen
Tangerang is a major urban and industrial city in Indonesia located just west of Jakarta on the island of Java.
-
B.
Cilegon
Cilegon is an industrial port city in western Java, Indonesia, known for its steel industry and strategic location near the Sunda Strait.
-
C.
Serang
Serang is the capital city of Banten Province on the western tip of Java, Indonesia, serving as an important regional administrative and economic center.
-
D.
Bekasi
Bekasi is a large, rapidly growing industrial and residential city in the Greater Jakarta metropolitan area of Indonesia.
-
E.
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.
- 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_69ad85e18c1c8190be8aafb227f39f48 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc46599188190a046eddb0d85c483 |
completed | March 8, 2026, 6:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4c3a50d40819081aad0c72bcaee9d |
completed | March 14, 2026, 2:10 a.m. |
Created at: March 8, 2026, 3:25 p.m.