Triple
T17195333
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CL-215T |
E417333
|
entity |
| Predicate | firefightingMethod |
P21100
|
FINISHED |
| Object | low-level water drop |
—
|
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: low-level water drop | Statement: [CL-215T, firefightingMethod, low-level water drop]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firefightingMethod Context triple: [CL-215T, firefightingMethod, low-level water drop]
-
A.
firefightingMethods
chosen
Indicates the techniques or strategies used to control, contain, or extinguish fires.
-
B.
firefightingResponse
Indicates the action of responding to and attempting to control or extinguish a fire or related emergency.
-
C.
methodOfKillingWildfire
Indicates the method or technique used to kill or extinguish a wildfire.
-
D.
fireRescue
Indicates a relationship where one entity performs or is responsible for rescuing people or property from fires or fire-related emergencies involving another entity.
-
E.
fireType
Indicates that one entity has a specific classification or category related to fire (e.g., type, kind, or nature of fire).
- 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_69d886d6ba8c819093215917b3d01689 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42da9e6208190b5c4e5925e840217 |
completed | April 19, 2026, 1:19 a.m. |
| PD | Predicate disambiguation | batch_69e383141ae0819096acd71683637cbc |
completed | April 18, 2026, 1:11 p.m. |
Created at: April 10, 2026, 5:38 a.m.