Triple
T38302574
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shanxi high-speed railway system |
E1032258
|
entity |
| Predicate | connectsToProvince |
P112692
|
FINISHED |
| Object | Hebei |
—
|
NE NERFINISHED |
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: Hebei | Statement: [Shanxi high-speed railway system, connectsToProvince, Hebei]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: connectsToProvince Context triple: [Shanxi high-speed railway system, connectsToProvince, Hebei]
-
A.
connectsProvinceOrRegion
chosen
Indicates that one entity serves to link or provide a connection between a specific province or region and another entity.
-
B.
connectsProvincesAlong
Indicates a relationship where something serves as a link or route joining multiple provinces along a specified path or alignment.
-
C.
connectsRemoteProvince
Indicates that something establishes or provides a link between a remote province and other places or systems.
-
D.
associatedWithProvinceCapital
Indicates that an entity has a relationship or connection to the capital city of a specific province.
-
E.
connectsToCityBy
Indicates that one entity is linked or has a direct connection to a specific city, such as through infrastructure, routes, or established relations.
- 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_69f76e0f2084819091299d021625c3fe |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a0019b0e9fc81909494320f05e81742 |
completed | May 10, 2026, 5:37 a.m. |
| PD | Predicate disambiguation | batch_6a00193379e0819096d1985686ce10e3 |
completed | May 10, 2026, 5:35 a.m. |
Created at: May 3, 2026, 4:30 p.m.