Triple
T4564481
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bandung railway station |
E121875
|
entity |
| Predicate | formerName |
P65
|
FINISHED |
| Object | Stasiun Bandung Hall |
E121875
|
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: Stasiun Bandung Hall | Statement: [Bandung railway station, formerName, Stasiun Bandung Hall]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stasiun Bandung Hall Context triple: [Bandung railway station, formerName, Stasiun Bandung Hall]
-
A.
Bandung railway station
chosen
Bandung railway station is the main rail hub in Bandung, Indonesia, serving as a key junction for intercity and regional train services across West Java and beyond.
-
B.
Bogor Station
Bogor Station is a major railway station and commuter rail terminus serving the city of Bogor and the greater Jakarta metropolitan area in Indonesia.
-
C.
Tasikmalaya railway station
Tasikmalaya railway station is a key train station in the city of Tasikmalaya, Indonesia, serving as an important regional hub for passenger rail transport.
-
D.
Bekasi Station
Bekasi Station is a major railway station serving commuter and intercity trains in the city of Bekasi, Indonesia.
-
E.
Sangen-jaya Station
Sangen-jaya Station is a major railway station in Tokyo’s Setagaya ward, serving as a busy transit hub with direct access to central city areas and a surrounding neighborhood known for its lively dining and nightlife.
- 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_69bd463f156881908a99aca69c5721ac |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd589b439c81908da9d19433310bcd |
completed | March 20, 2026, 2:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdc5a8ea5481908a652290e9df911f |
completed | March 20, 2026, 10:09 p.m. |
Created at: March 20, 2026, 1:09 p.m.