Triple
T19175551
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Camp Fire |
E469427
|
entity |
| Predicate | burnedArea |
P134739
|
FINISHED |
| Object | approximately 153,336 acres |
—
|
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: approximately 153,336 acres | Statement: [Camp Fire, burnedArea, approximately 153,336 acres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: burnedArea Context triple: [Camp Fire, burnedArea, approximately 153,336 acres]
-
A.
burnedDuring
Indicates that one event, object, or process was actively burning or being consumed by fire during the time span of another specified event or interval.
-
B.
burned
Indicates that one entity caused another entity to be damaged or consumed by fire or intense heat.
-
C.
burnsIn
Indicates that one entity undergoes combustion or is consumed by fire within or at the location of another entity.
-
D.
burns
Indicates that one entity is consuming or damaging another through fire or intense heat.
-
E.
burnType
Indicates the specific category or severity of a burn associated with an entity or event.
- 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_69d8dd09d5a081909ae43c286651ae5a |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5f166d3888190adaf6dc8531a8ed1 |
completed | April 20, 2026, 9:27 a.m. |
| PD | Predicate disambiguation | batch_69e4b9bb158481909478ca2e06f3ba39 |
completed | April 19, 2026, 11:17 a.m. |
| PDg | Predicate description generation | batch_69e4bfe9ef7081908a74a57d1fc731ea |
completed | April 19, 2026, 11:43 a.m. |
Created at: April 10, 2026, 12:06 p.m.