Triple
T9060422
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thomas Fire |
E217106
|
entity |
| Predicate | structuresDamaged |
P54661
|
FINISHED |
| Object | hundreds of structures damaged |
—
|
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: hundreds of structures damaged | Statement: [Thomas Fire, structuresDamaged, hundreds of structures damaged]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: structuresDamaged Context triple: [Thomas Fire, structuresDamaged, hundreds of structures damaged]
-
A.
infrastructureDamage
chosen
Indicates damage or destruction affecting physical infrastructure such as buildings, roads, utilities, or other constructed facilities.
-
B.
damagedIn
Indicates that an entity has suffered harm, impairment, or destruction as a result of a specified event, process, or condition.
-
C.
buildingsDestroyed
Indicates that one or more buildings have been damaged to the point of destruction as a result of some event or action.
-
D.
damagedBy
Indicates that one entity has caused harm, impairment, or deterioration to another entity.
-
E.
hasDemolitionOrDestruction
Indicates that one entity causes, undergoes, or is associated with the demolition or destruction of another entity.
- 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_69ca83d4425481909a319dab847724ec |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc7eca6d8c8190b1a11a60d6649f78 |
completed | April 1, 2026, 2:11 a.m. |
| PD | Predicate disambiguation | batch_69cc5ee6d83c819095d8ed0779aa8511 |
completed | March 31, 2026, 11:55 p.m. |
Created at: March 30, 2026, 7:10 p.m.