Triple
T23540815
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daniel Cerone |
E577740
|
entity |
| Predicate | workedOn |
P3
|
FINISHED |
| Object | Clubhouse |
—
|
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: Clubhouse | Statement: [Daniel Cerone, workedOn, Clubhouse]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Clubhouse Context triple: [Daniel Cerone, workedOn, Clubhouse]
-
A.
Clubhouse
chosen
Clubhouse is a 2004 American drama film in which John Ortiz appears, centered on a troubled teen who finds an unlikely sense of belonging in a minor league baseball team’s clubhouse.
-
B.
Chatterbug
Chatterbug is an online language-learning platform that offers live tutoring and interactive exercises to help users practice and improve foreign language skills.
-
C.
Peeplu
Peeplu is a small town in the Tonk district of Rajasthan, India, known for its rural setting and local agricultural economy.
-
D.
Parler
Parler is a social media platform known for its emphasis on minimal content moderation and popularity among right-wing and conservative users.
-
E.
Mumble
Mumble is the tap-dancing emperor penguin protagonist of the animated film "Happy Feet."
- 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_69e245f9d5d08190a4a20004e1784e20 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f1ae1b3a8c8190b5b6a58f0476c5d2 |
completed | April 29, 2026, 7:07 a.m. |
Created at: April 17, 2026, 6:10 p.m.