Triple
T3158458
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jean Hagen as Lina Lamont |
E66045
|
entity |
| Predicate | toneContribution |
P29850
|
FINISHED |
| Object | slapstickComedy |
—
|
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: slapstickComedy | Statement: [Jean Hagen as Lina Lamont, toneContribution, slapstickComedy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: toneContribution Context triple: [Jean Hagen as Lina Lamont, toneContribution, slapstickComedy]
-
A.
tone
Indicates the characteristic attitude or emotional quality expressed in how something is communicated or presented.
-
B.
tonalCenter
Indicates that one musical element functions as the primary pitch or key center around which another musical element is organized.
-
C.
tuning
Indicates the adjustment or calibration of something’s parameters or settings to achieve desired performance or behavior.
-
D.
vocalizationCharacteristic
chosen
Indicates how an entity’s vocal sounds are characterized, such as their quality, style, or distinctive acoustic features.
-
E.
hasPhonemicTone
Indicates that a language, word, or syllable uses pitch differences (tones) as phonemic contrasts that can change meaning.
- 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_69ad85850c1481908a9e9c6242238de2 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada5ed82a08190a1bdcf18ee593c79 |
completed | March 8, 2026, 4:38 p.m. |
| PD | Predicate disambiguation | batch_69ad9dfbf0348190952a6bca8fc5fed1 |
completed | March 8, 2026, 4:04 p.m. |
Created at: March 8, 2026, 3:05 p.m.