Triple
T29825713
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sichuan earthquake |
E757370
|
entity |
| Predicate | worstHitArea |
P97584
|
FINISHED |
| Object | Wenchuan 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: Wenchuan County | Statement: [Sichuan earthquake, worstHitArea, Wenchuan County]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: worstHitArea Context triple: [Sichuan earthquake, worstHitArea, Wenchuan County]
-
A.
hardestHitRegion
chosen
Indicates the region that experienced the greatest impact or severity compared to all other regions.
-
B.
worstHitDistrict
Indicates the district that is most severely affected or damaged in a given event or context.
-
C.
worstHitCity
Indicates that a city is the one most severely affected or damaged in a particular event or context compared to other cities.
-
D.
worstHitTown
Indicates that a town is the one most severely affected or damaged in a particular event or disaster.
-
E.
regionOfLoser
Indicates the geographic region or area from which the losing party in an event, contest, or comparison originates.
- 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.