Triple
T7809492
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hot Water |
E180640
|
entity |
| Predicate | featuresVehicleGags |
P58285
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Hot Water, featuresVehicleGags, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresVehicleGags Context triple: [Hot Water, featuresVehicleGags, true]
-
A.
featuresVehicle
Indicates that one entity includes, presents, or prominently incorporates a particular vehicle as part of its content, composition, or offering.
-
B.
featuresRunningGag
chosen
Indicates that the subject includes or makes use of a recurring joke or humorous motif.
-
C.
gimmick
Indicates that an entity uses or features a novel, attention-grabbing trick or device primarily intended to attract interest rather than provide substantive value.
-
D.
notableGag
Indicates that something features a particularly memorable or significant joke, comedic moment, or running gag.
-
E.
noseGearFeature
Indicates that there is a specific characteristic, component, or design attribute associated with the nose landing gear of an aircraft.
- 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_69ca827f6f148190beca4e245b993506 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69caf78bb4b08190b2b3b51c5a0a033c |
completed | March 30, 2026, 10:22 p.m. |
| PD | Predicate disambiguation | batch_69cae91687788190af9cb7aaa996d291 |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 4:36 p.m.