Triple
T15028861
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pedrógão Grande |
E378287
|
entity |
| Predicate | injuriesIn2017Wildfires |
P116448
|
FINISHED |
| Object | dozens of injuries |
—
|
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: dozens of injuries | Statement: [Pedrógão Grande, injuriesIn2017Wildfires, dozens of injuries]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: injuriesIn2017Wildfires Context triple: [Pedrógão Grande, injuriesIn2017Wildfires, dozens of injuries]
-
A.
wildfireRisk
Indicates the likelihood or potential severity of wildfires occurring in a given area or under specific conditions.
-
B.
bushfiresImpact
Indicates how bushfires affect or influence other entities, conditions, or outcomes.
-
C.
methodOfKillingWildfire
Indicates the method or technique used to kill or extinguish a wildfire.
-
D.
numberOfFirefightersInvolved
Indicates the total count of firefighters who participated in or were involved in a specific event, incident, or operation.
-
E.
numberOfPeopleLaterDyingOfInjuriesConsidered
Indicates the number of people who subsequently died from injuries that were previously evaluated or taken into account.
- 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_69d85cd46b2c819090d054c27787f677 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded7e0e8c88190ac6f5786b4d4040f |
completed | April 15, 2026, 12:12 a.m. |
| PD | Predicate disambiguation | batch_69de9a67cbc481909c19c2de57de4eb7 |
completed | April 14, 2026, 7:50 p.m. |
| PDg | Predicate description generation | batch_69deb1a88d588190996afa8e5b32b552 |
completed | April 14, 2026, 9:29 p.m. |
Created at: April 10, 2026, 2:58 a.m.