Triple
T15710141
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Scream (2022 film) |
E380815
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object | Courteney Cox |
E330421
|
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: Courteney Cox | Statement: [Scream (2022 film), stars, Courteney Cox]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Courteney Cox Context triple: [Scream (2022 film), stars, Courteney Cox]
-
A.
Courteney Cox
chosen
Courteney Cox is an American actress best known for playing Monica Geller on the hit television sitcom "Friends."
-
B.
Shelley Long
Shelley Long is an American actress best known for her Emmy-winning role as Diane Chambers on the television sitcom "Cheers."
-
C.
Kirstie Alley
Kirstie Alley was an American actress best known for her Emmy-winning role as Rebecca Howe on the hit sitcom "Cheers" and for her work in films like "Look Who's Talking."
-
D.
Laraine Newman
Laraine Newman is an American comedian and actress best known as one of the original cast members of Saturday Night Live in the 1970s.
-
E.
Megan Mullally
Megan Mullally is an American actress, comedian, and singer best known for her Emmy-winning role as Karen Walker on the television sitcom "Will & Grace."
- 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_69d86d9bf930819082b30cf6d169297c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e04f8d8b648190842c635f2ae7bfa4 |
completed | April 16, 2026, 2:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff87657528819098880c84f7cb1610 |
completed | May 9, 2026, 7:13 p.m. |
Created at: April 10, 2026, 4:45 a.m.