Triple
T4159964
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Canadair CL-215 |
E91506
|
entity |
| Predicate | canScoopWaterInFlight |
P54164
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Canadair CL-215, canScoopWaterInFlight, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: canScoopWaterInFlight Context triple: [Canadair CL-215, canScoopWaterInFlight, yes]
-
A.
landingCapability
Indicates the ability or suitability of an entity (e.g., a vehicle or system) to perform a landing under specified conditions.
-
B.
canHold
Indicates that one entity has the capacity or ability to contain, support, or carry another entity.
-
C.
hasCrewCapacity
Indicates that an entity is capable of accommodating a specified number of crew members.
-
D.
canSitIn
Indicates that one entity is able or permitted to sit inside or occupy the seating space of another entity.
-
E.
tookOnWater
Indicates that an entity began to fill or absorb water, typically in an unintended or problematic way (e.g., a vessel leaking or flooding).
- 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_69aed9626ebc8190a39de631788bea3e |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af0321eee88190871c1d4bf44a5007 |
completed | March 9, 2026, 5:28 p.m. |
| PD | Predicate disambiguation | batch_69af018dc90c8190a754b1bfbc802e80 |
completed | March 9, 2026, 5:21 p.m. |
| PDg | Predicate description generation | batch_69af0320775c8190b90d80f512060f1c |
completed | March 9, 2026, 5:28 p.m. |
Created at: March 9, 2026, 3:44 p.m.