Triple
T25096367
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2016 Kumamoto earthquakes |
E628599
|
entity |
| Predicate | causedTransportDisruption |
P158776
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [2016 Kumamoto earthquakes, causedTransportDisruption, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: causedTransportDisruption Context triple: [2016 Kumamoto earthquakes, causedTransportDisruption, true]
-
A.
transportationImpact
Indicates how one entity’s transportation-related activities or characteristics affect another entity or the surrounding environment.
-
B.
roadAffected
Indicates that a road is impacted or disrupted by a condition, event, or action, such as construction, accidents, or adverse weather.
-
C.
causedAccident
Indicates that one entity is responsible for bringing about or initiating an accident involving another entity or situation.
-
D.
cityTransportOperatorAffected
Indicates that a city transport operator is impacted or influenced by a particular event, condition, or circumstance.
-
E.
disruptedIn
Indicates that a normal process, function, or state is interrupted, impaired, or thrown into disorder within the specified context.
- F. None of above. chosen
Provenance (4 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_69e2ff2f58e881908340527bc5d34f07 |
completed | April 18, 2026, 3:49 a.m. |
| NER | Named-entity recognition | batch_69f48b9b687881908fd87a2f5fa0b1e7 |
completed | May 1, 2026, 11:16 a.m. |
| PD | Predicate disambiguation | batch_69f48060597c8190a4414e4e4fcb1fec |
completed | May 1, 2026, 10:28 a.m. |
| PDg | Predicate description generation | batch_69f48b9058d081908ec9af261ee092e2 |
completed | May 1, 2026, 11:16 a.m. |
Created at: April 18, 2026, 6:25 a.m.