Triple
T2232618
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | George Lopez |
E49202
|
entity |
| Predicate | comedyFocus |
P14479
|
FINISHED |
| Object | Mexican-American culture |
—
|
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: Mexican-American culture | Statement: [George Lopez, comedyFocus, Mexican-American culture]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: comedyFocus Context triple: [George Lopez, comedyFocus, Mexican-American culture]
-
A.
parodies
Indicates that one entity imitates another in an exaggerated or humorous way, often to criticize or comment on the original.
-
B.
entertainmentType
Indicates the kind or category of entertainment associated with an entity or event.
-
C.
hasHumorType
chosen
Indicates that an entity possesses or is characterized by a particular style, category, or type of humor.
-
D.
shows
Indicates that one entity presents, displays, or makes another entity visible or known to an audience or observer.
-
E.
theater
Indicates that an entity is a theater or is functioning in the role of a theater (a venue where performances or films are shown).
- 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_69a88aa84bdc819086df50e9c20b301e |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc06d26bc8190a85ddb6312d2df08 |
completed | March 7, 2026, 6:06 a.m. |
| PD | Predicate disambiguation | batch_69abbdadbb0c8190b3a1ede31b8acbfa |
completed | March 7, 2026, 5:54 a.m. |
Created at: March 4, 2026, 7:47 p.m.