Triple
T17305865
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Martin Lawrence |
E420162
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Rebound |
E604840
|
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: Rebound | Statement: [Martin Lawrence, notableWork, Rebound]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rebound Context triple: [Martin Lawrence, notableWork, Rebound]
-
A.
Rebound
chosen
Rebound is a 2005 sports comedy film starring Martin Lawrence as a disgraced college basketball coach who must redeem himself by leading a misfit middle school team.
-
B.
The Rebound
The Rebound is a 2009 romantic comedy film starring Catherine Zeta-Jones and Justin Bartha about an unexpected relationship between a newly divorced mother and her younger babysitter.
-
C.
Bounce Back
"Bounce Back" is a 2016 hit single by American rapper Big Sean known for its motivational theme about recovering from setbacks and its strong commercial success.
-
D.
Bounce
"Bounce" is a popular Afrobeats song by Nigerian singer Rema, known for its energetic production and catchy, dance-oriented style.
-
E.
Bounce
Bounce is a popular brand of fabric softener dryer sheets known for reducing static cling and adding fragrance to laundry.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d889d22b848190a4663d0b8f8f76e7 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e438ff3ee08190ab4c44a22f86b38b |
completed | April 19, 2026, 2:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0180e0c1b881908aa2b6b4d8ac04b6 |
completed | May 11, 2026, 7:10 a.m. |
Created at: April 10, 2026, 5:43 a.m.