Triple
T10339079
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hawkeye Pierce |
E243081
|
entity |
| Predicate | usesHumorAs |
P14479
|
FINISHED |
| Object | coping mechanism |
—
|
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: coping mechanism | Statement: [Hawkeye Pierce, usesHumorAs, coping mechanism]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesHumorAs Context triple: [Hawkeye Pierce, usesHumorAs, coping mechanism]
-
A.
humorSetting
Indicates a relationship where one entity specifies or controls the level, style, or presence of humor applied to another entity or context.
-
B.
hasHumorType
chosen
Indicates that an entity possesses or is characterized by a particular style, category, or type of humor.
-
C.
hasHumorousTreatmentOf
Indicates that one entity presents or portrays another entity in a humorous, comedic, or joking manner.
-
D.
usesDoubleEntendre
Indicates that one entity employs language or expressions with a double meaning, often to convey a hidden or suggestive message alongside a literal one.
-
E.
isHumorousCharacter
Indicates that the character is portrayed in a humorous way or primarily serves a comedic role in the context.
- 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_69d381af787481908bc401325c760a88 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e91fdb2081909866c6ecf417d75a |
completed | April 7, 2026, 11:23 a.m. |
| PD | Predicate disambiguation | batch_69d4df9dc3208190bf1bd106f44f6202 |
completed | April 7, 2026, 10:42 a.m. |
Created at: April 6, 2026, 11:54 a.m.