Triple
T23103101
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zack Pearlman |
E576085
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Mulaney |
—
|
NE NERFINISHED |
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: Mulaney | Statement: [Zack Pearlman, notableWork, Mulaney]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mulaney Context triple: [Zack Pearlman, notableWork, Mulaney]
-
A.
Mulaney
chosen
Mulaney is a short-lived semi-autobiographical sitcom created by and starring comedian John Mulaney.
-
B.
Mulanay
Mulanay is a coastal agricultural municipality in the southern part of Quezon Province in the Philippines, known for its rural landscapes and farming communities.
-
C.
Latee
Latee is a hip-hop artist known for his work with influential producer The 45 King during the late 1980s and early 1990s underground rap scene.
-
D.
Maretha
Maretha is a young girl in the television film adaptation of August Wilson's "The Piano Lesson," representing the family's next generation and their hopes for a better future.
-
E.
Tinée
Tinée is a river in southeastern France that flows through the Alpes-Maritimes department in the Provence-Alpes-Côte d'Azur region.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245c060b48190a9bd61a47a16db17 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18deaacbc8190a97e64e1cf39cdd6 |
completed | April 29, 2026, 4:49 a.m. |
Created at: April 17, 2026, 3:58 p.m.