Triple
T17763467
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baywatch: Hawaiian Wedding |
E443440
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object |
Stacy Kamano
Stacy Kamano is an American actress best known for playing lifeguard Kekoa Tanaka on the television series Baywatch and its follow-up film Baywatch: Hawaiian Wedding.
|
E1286214
|
NE FINISHED |
How this triple was built (4 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: Stacy Kamano | Statement: [Baywatch: Hawaiian Wedding, stars, Stacy Kamano]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stacy Kamano Context triple: [Baywatch: Hawaiian Wedding, stars, Stacy Kamano]
-
A.
Kimberly Steward
Kimberly Steward is an American film producer best known for her work on acclaimed independent films, including the Oscar-winning drama "Manchester by the Sea."
-
B.
Stacey Thomas
Stacey Thomas is known as the spouse of American actor Robert Foxworth.
-
C.
Stacy Barrett
Stacy Barrett is a bubbly, loyal, and somewhat ditzy best friend character from the legal comedy-drama TV series "Drop Dead Diva."
-
D.
Stacey Menear
Stacey Menear is a screenwriter best known for writing the horror film "The Boy" (2016).
-
E.
Kia Stevens
Kia Stevens is a professional wrestler and actress best known for her powerful in-ring persona Awesome Kong and her role on the Netflix series GLOW.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Stacy Kamano Triple: [Baywatch: Hawaiian Wedding, stars, Stacy Kamano]
Generated description
Stacy Kamano is an American actress best known for playing lifeguard Kekoa Tanaka on the television series Baywatch and its follow-up film Baywatch: Hawaiian Wedding.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Stacy Kamano Target entity description: Stacy Kamano is an American actress best known for playing lifeguard Kekoa Tanaka on the television series Baywatch and its follow-up film Baywatch: Hawaiian Wedding.
-
A.
Kimberly Steward
Kimberly Steward is an American film producer best known for her work on acclaimed independent films, including the Oscar-winning drama "Manchester by the Sea."
-
B.
Stacey Thomas
Stacey Thomas is known as the spouse of American actor Robert Foxworth.
-
C.
Stacy Barrett
Stacy Barrett is a bubbly, loyal, and somewhat ditzy best friend character from the legal comedy-drama TV series "Drop Dead Diva."
-
D.
Stacey Menear
Stacey Menear is a screenwriter best known for writing the horror film "The Boy" (2016).
-
E.
Kia Stevens
Kia Stevens is a professional wrestler and actress best known for her powerful in-ring persona Awesome Kong and her role on the Netflix series GLOW.
- F. None of above. chosen
Provenance (5 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_69d8b9edf16c8190a59ebd245d378f4f |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e485f9fa188190aef5ffeb0b628632 |
completed | April 19, 2026, 7:36 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a02efbcd5948190882b2a138c064f3a |
completed | May 12, 2026, 9:15 a.m. |
| NEDg | Description generation | batch_6a02f04731e8819092751711a70b4f1b |
completed | May 12, 2026, 9:17 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a02f116f4648190ac9ef1cc81bd032d |
completed | May 12, 2026, 9:21 a.m. |
Created at: April 10, 2026, 10:11 a.m.