Triple
T24775282
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kaohsiung MRT Red Line |
E619843
|
entity |
| Predicate | numberOfMainCorridorsInSystem |
P4095
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Kaohsiung MRT Red Line, numberOfMainCorridorsInSystem, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfMainCorridorsInSystem Context triple: [Kaohsiung MRT Red Line, numberOfMainCorridorsInSystem, 2]
-
A.
numberOfCorridors
chosen
Indicates the total count of corridors associated with or contained within a given entity or structure.
-
B.
lengthOfCorridors
Indicates the measured extent or distance of corridors within a given space or structure.
-
C.
isPartOfCorridorSystem
Indicates that one entity forms a component or segment within a larger interconnected corridor system.
-
D.
otherMainCorridor
Indicates that one corridor is an alternative or secondary main corridor relative to another within the same structure or network.
-
E.
isMostHeavilyTraveledCorridorIn
Indicates that a particular route or corridor experiences the highest volume of travel or traffic within a specified area or region.
- 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_69e2fabd04488190a2d13c97be745a2d |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f62d89b89c8190afb372a8172111e7 |
completed | May 2, 2026, 4:59 p.m. |
| PD | Predicate disambiguation | batch_69f62c1379f08190836c3e02b0c892df |
completed | May 2, 2026, 4:53 p.m. |
Created at: April 18, 2026, 4:34 a.m.