Triple
T25191541
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jatinegara Station |
E630884
|
entity |
| Predicate | railwayLine |
P848
|
FINISHED |
| Object |
Jakarta commuter rail network
The Jakarta commuter rail network is a suburban railway system serving Greater Jakarta and surrounding regions, providing high-capacity, frequent transit for daily commuters.
|
E1673812
|
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: Jakarta commuter rail network | Statement: [Jatinegara Station, railwayLine, Jakarta commuter rail network]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Jakarta commuter rail network Triple: [Jatinegara Station, railwayLine, Jakarta commuter rail network]
Generated description
The Jakarta commuter rail network is a suburban railway system serving Greater Jakarta and surrounding regions, providing high-capacity, frequent transit for daily commuters.
Provenance (5 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_69e75a8a6d088190ba1e82a4345225e7 |
completed | April 21, 2026, 11:07 a.m. |
| NER | Named-entity recognition | batch_69f46e0f69b081908b72abcd18ab9c67 |
completed | May 1, 2026, 9:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a1067ca477c8190b7ca9500d152074e |
completed | May 22, 2026, 2:27 p.m. |
| NEDg | Description generation | batch_6a106883259c8190a5cd5759a46c4c40 |
completed | May 22, 2026, 2:30 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a106b36ea6481908bd4a4ead6b40818 |
completed | May 22, 2026, 2:41 p.m. |
Created at: April 21, 2026, 12:45 p.m.