Triple
T25805507
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daegu subway fire |
E649957
|
entity |
| Predicate | vehicleInvolved |
P12443
|
FINISHED |
| Object | Train 1080 |
—
|
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: Train 1080 | Statement: [Daegu subway fire, vehicleInvolved, Train 1080]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: vehicleInvolved Context triple: [Daegu subway fire, vehicleInvolved, Train 1080]
-
A.
utilityInvolved
Indicates that a utility service or provider is involved in, associated with, or plays a role in the referenced situation or relationship.
-
B.
vehicleBase
Indicates that one entity serves as the foundational or underlying base for a vehicle-related entity or system.
-
C.
vehicleUsed
chosen
Indicates that a particular vehicle is utilized or employed in performing an action, event, or activity.
-
D.
carriagesInvolved
Indicates that specific carriages are participants in, or affected by, the referenced event or situation.
-
E.
depictsVehicle
Indicates that one entity visually represents or portrays a vehicle in an image, artwork, or other depiction.
- 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_69e7ab35d264819095367f7e80c983ff |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f603bb02288190b40cedbed5b9651d |
completed | May 2, 2026, 2:01 p.m. |
| PD | Predicate disambiguation | batch_69f602d07590819085ac34b189613104 |
completed | May 2, 2026, 1:57 p.m. |
Created at: April 22, 2026, 7:02 a.m.