Triple
T29825714
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sichuan earthquake |
E757370
|
entity |
| Predicate | severelyAffectedArea |
P1586
|
FINISHED |
| Object | Beichuan County |
—
|
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: Beichuan County | Statement: [Sichuan earthquake, severelyAffectedArea, Beichuan County]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: severelyAffectedArea Context triple: [Sichuan earthquake, severelyAffectedArea, Beichuan County]
-
A.
affectedArea
chosen
Indicates the specific region or extent over which an event, condition, or influence has an impact.
-
B.
affectsAnatomicalLocation
Indicates that one entity produces an effect on, or has an impact at, a specific anatomical location.
-
C.
sectorMostAffected
Indicates that a particular sector is the one experiencing the greatest impact or disruption relative to others in a given context.
-
D.
burnedArea
Indicates the extent or portion of an area that has been affected or consumed by fire.
-
E.
occupiedRegion
Indicates that an entity has taken control of and is currently holding a specific geographic 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_69f22457c84c8190a6d9f56bc74082a9 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f67597ec4081909c0b453a69f14c28 |
completed | May 2, 2026, 10:07 p.m. |
| PD | Predicate disambiguation | batch_69f66ac32b60819092290b2de35988d3 |
completed | May 2, 2026, 9:21 p.m. |
Created at: April 29, 2026, 5:31 p.m.