Triple
T18950055
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Louie |
E463621
|
entity |
| Predicate | hasNotableTone |
P49759
|
FINISHED |
| Object | blend of comedy and drama |
—
|
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: blend of comedy and drama | Statement: [Louie, hasNotableTone, blend of comedy and drama]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableTone Context triple: [Louie, hasNotableTone, blend of comedy and drama]
-
A.
hasNotablePhrase
Indicates that an entity is associated with a specific phrase or expression that is considered notable or characteristic of it.
-
B.
hasNotableWord
Indicates that an entity is associated with a word or term that is considered notable, distinctive, or significant in some context.
-
C.
contributesToTone
chosen
Indicates that one entity plays a role in shaping, influencing, or determining the overall tone or mood of another entity.
-
D.
hasIntrospectiveTone
Indicates that the subject expresses thoughts, feelings, or experiences in a reflective, inward-looking manner.
-
E.
hasNotableSentence
Indicates that an entity is associated with a particularly important, famous, or otherwise noteworthy sentence.
- 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_69d8dcfec90481909e926be9767e5779 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d542b238819089ccd2df279a2f7f |
completed | April 20, 2026, 7:26 a.m. |
| PD | Predicate disambiguation | batch_69e4a2efec5c8190840704016bf547a1 |
completed | April 19, 2026, 9:40 a.m. |
Created at: April 10, 2026, noon