Triple
T4458214
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pamela Anderson |
E98183
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Baywatch |
E390545
|
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: Baywatch | Statement: [Pamela Anderson, notableWork, Baywatch]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Baywatch Context triple: [Pamela Anderson, notableWork, Baywatch]
-
A.
Baywatch
chosen
Baywatch is a popular American television series centered on the dramatic and action-filled lives of Los Angeles County lifeguards patrolling the beaches of Southern California.
-
B.
Baywatch (2017 film)
Baywatch (2017 film) is a 2017 action-comedy movie adaptation of the classic lifeguard TV series, starring Dwayne Johnson and Zac Efron.
-
C.
China Beach
China Beach is a small, scenic cove and sandy beach in San Francisco known for its views of the Golden Gate Bridge and the Marin Headlands.
-
D.
China Beach
China Beach is an American television drama series set during the Vietnam War that focuses on the lives of medical and military personnel at an evacuation hospital.
-
E.
Splash, Too
Splash, Too is a 1988 made-for-television sequel to the romantic fantasy film Splash, continuing the story of a man and the mermaid he loves.
- 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_69b3454a7c608190944f5455c8031d73 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3564485688190a0d49fdccf8724be |
completed | March 13, 2026, 12:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b6282ea6308190b726a9f8176d4d1b |
completed | March 15, 2026, 3:31 a.m. |
Created at: March 12, 2026, 11:33 p.m.