Triple
T3536358
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kevin Spacey as Dave Harken |
E74781
|
entity |
| Predicate | toneOfCharacter |
P7344
|
FINISHED |
| Object | darkly comic |
—
|
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: darkly comic | Statement: [Kevin Spacey as Dave Harken, toneOfCharacter, darkly comic]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: toneOfCharacter Context triple: [Kevin Spacey as Dave Harken, toneOfCharacter, darkly comic]
-
A.
tone
chosen
Indicates the characteristic attitude or emotional quality expressed in how something is communicated or presented.
-
B.
voiceType
Indicates the specific vocal style, quality, or role associated with an entity’s voice in a given context.
-
C.
moralTone
Indicates the evaluative moral quality or ethical character expressed in or associated with an action, statement, or situation.
-
D.
characterContrast
Indicates a relationship where two characters are compared to highlight their opposing or significantly differing traits, roles, or behaviors.
-
E.
tonalCharacteristic
Indicates the specific quality or character of a sound’s tone, such as its color, texture, or expressive nuance, in relation to an 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_69ad85d1a3948190931fd1ea1f49717b |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbcc7b92481908d2d99948780f4d0 |
completed | March 8, 2026, 6:15 p.m. |
| PD | Predicate disambiguation | batch_69adae13ab808190a5d6ecdc7543445e |
completed | March 8, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:20 p.m.