Triple
T34781741
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | KCUF |
E1002682
|
entity |
| Predicate | hasOnAirPersonality |
P5272
|
FINISHED |
| Object | Mucho Maas |
E305352
|
NE 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: Mucho Maas | Statement: [KCUF, hasOnAirPersonality, Mucho Maas]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOnAirPersonality Context triple: [KCUF, hasOnAirPersonality, Mucho Maas]
-
A.
notableOnAirPersonality
chosen
Indicates that an entity is recognized as a prominent or distinguished on-air media personality associated with another entity (such as a show, station, or network).
-
B.
hasMainPersonality
Indicates that one entity possesses or is characterized by a primary or dominant personality associated with another entity.
-
C.
hasDifferentPersonalityIn
Indicates that an entity exhibits a different personality or character traits within a specified context, setting, or situation.
-
D.
hasPersona
Indicates that an entity possesses or is associated with a particular persona, role, or character profile.
-
E.
characterInFocus
Indicates that a particular character is the primary subject or focal point within a given context, scene, or narrative segment.
- F. None of above.
Provenance (4 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_69f76db30a108190bb57ca95b873e5bb |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69fbbc49da8c8190902bbb05d2477cab |
completed | May 6, 2026, 10:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a376642e6708190aa64aa53544c7350 |
completed | June 21, 2026, 4:19 a.m. |
| PD | Predicate disambiguation | batch_69fbb13f34b08190bbbb220ac1e6e666 |
completed | May 6, 2026, 9:23 p.m. |
Created at: May 3, 2026, 3:59 p.m.