Triple
T8659071
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Xiamen–Shenzhen Railway |
E205499
|
entity |
| Predicate | belongsToTransportSector |
P23255
|
FINISHED |
| Object | rail transport in China |
—
|
LITERAL 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: rail transport in China | Statement: [Xiamen–Shenzhen Railway, belongsToTransportSector, rail transport in China]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: belongsToTransportSector Context triple: [Xiamen–Shenzhen Railway, belongsToTransportSector, rail transport in China]
-
A.
associatedWithEconomicSector
Indicates that an entity has a connection or involvement with a particular economic sector, such as operating, participating, or being relevant within that sector.
-
B.
transportationSector
chosen
Indicates a relationship where an entity is involved in, associated with, or classified as part of the transportation sector or transportation-related activities.
-
C.
ownerSector
Indicates the sector or industry category to which the owner of an entity belongs.
-
D.
hasOccupationSector
Indicates that an entity’s occupation belongs to or is categorized within a particular economic or professional sector.
-
E.
isSectorSpecific
Indicates that something is tailored or restricted to a particular industry or sector rather than being generally applicable.
- F. None of above.
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_69ca8350897c819086cde7596fbe5fe7 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc486ece68819089c74bdf98b64490 |
completed | March 31, 2026, 10:19 p.m. |
| PD | Predicate disambiguation | batch_69cc4564e018819081036722f3e42a71 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:30 p.m.