Triple
T5886247
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Liuzhou–Nanning intercity railway |
E130868
|
entity |
| Predicate | isInTransportSector |
P23255
|
FINISHED |
| Object | railway transport |
—
|
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: railway transport | Statement: [Liuzhou–Nanning intercity railway, isInTransportSector, railway transport]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isInTransportSector Context triple: [Liuzhou–Nanning intercity railway, isInTransportSector, railway transport]
-
A.
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.
-
B.
hasOccupationSector
Indicates that an entity’s occupation belongs to or is categorized within a particular economic or professional sector.
-
C.
isPartOfIndustry
Indicates that one entity belongs to, operates within, or is categorized under a particular industry sector.
-
D.
ownerSector
Indicates the sector or industry category to which the owner of an entity belongs.
-
E.
containsIndustry
Indicates that one entity includes or encompasses a particular industry within its scope, structure, or operations.
- 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_69c0085628dc8190b334c1b44c067efc |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03fe07b7081909f8577ec3a9a1a8d |
completed | March 22, 2026, 7:15 p.m. |
| PD | Predicate disambiguation | batch_69c0334bdc308190ad0d7199ab975588 |
completed | March 22, 2026, 6:22 p.m. |
Created at: March 22, 2026, 3:57 p.m.