Triple
T37839711
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wuhan metropolitan transport network |
E943437
|
entity |
| Predicate | connectsVia |
P845
|
FINISHED |
| Object |
Wuhan–Xianning intercity railway
The Wuhan–Xianning intercity railway is a regional rail line in Hubei, China that links the city of Wuhan with nearby Xianning as part of the area’s suburban commuter network.
|
E2245893
|
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: Wuhan–Xianning intercity railway | Statement: [Wuhan metropolitan transport network, connectsVia, Wuhan–Xianning intercity railway]
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: Wuhan–Xianning intercity railway Triple: [Wuhan metropolitan transport network, connectsVia, Wuhan–Xianning intercity railway]
Generated description
The Wuhan–Xianning intercity railway is a regional rail line in Hubei, China that links the city of Wuhan with nearby Xianning as part of the area’s suburban commuter network.
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_69f76eeb0f7081908d6d3adbc469889c |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fbb1f4b7f48190a6228ddf7b5c9c4a |
completed | May 6, 2026, 9:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a41041815fc81909e152b0d677b6237 |
completed | June 28, 2026, 11:23 a.m. |
| NEDg | Description generation | batch_6a4104850d7081908593da7a59d0d6b4 |
completed | June 28, 2026, 11:24 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a4104eba3648190946cab1b84976a74 |
completed | June 28, 2026, 11:26 a.m. |
Created at: May 3, 2026, 4:19 p.m.