Triple
T25805504
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daegu subway fire |
E649957
|
entity |
| Predicate | deadliestUrbanRailDisasterInCountry |
P152432
|
FINISHED |
| Object | South Korea |
—
|
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: South Korea | Statement: [Daegu subway fire, deadliestUrbanRailDisasterInCountry, South Korea]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: deadliestUrbanRailDisasterInCountry Context triple: [Daegu subway fire, deadliestUrbanRailDisasterInCountry, South Korea]
-
A.
deadliestRailAccidentInJapanSince
Indicates that a rail accident is the most lethal one in Japan occurring on or after a specified date or event.
-
B.
railDisasterYear
Indicates the year in which a rail-related disaster occurred.
-
C.
otherMajorTragedy
Indicates that the subject experienced or was involved in a significant tragic event other than the primary or most notable tragedy under consideration.
-
D.
oneOfWorstDisastersIn
chosen
Indicates that an event is among the most severe or catastrophic disasters that have occurred within a specified place or region.
-
E.
isUrbanRailwayIn
Indicates that an urban railway system or line is located within or operates inside a specified geographic or administrative area.
- 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_69f5ffcee8288190b03d20d2f1f8df3d |
completed | May 2, 2026, 1:44 p.m. |
| PD | Predicate disambiguation | batch_69f4938b960081909b53c074a3e0c7c2 |
completed | May 1, 2026, 11:50 a.m. |
Created at: April 22, 2026, 7:02 a.m.