Triple
T11687373
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Melissa McCarthy |
E277777
|
entity |
| Predicate | birthName |
P65
|
FINISHED |
| Object | Melissa Ann McCarthy |
E277777
|
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: Melissa Ann McCarthy | Statement: [Melissa McCarthy, birthName, Melissa Ann McCarthy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Melissa Ann McCarthy Context triple: [Melissa McCarthy, birthName, Melissa Ann McCarthy]
-
A.
Melissa McCarthy
chosen
Melissa McCarthy is an American actress and comedian known for her breakout comedic role in "Bridesmaids" and subsequent work in film and television.
-
B.
Kristen Wiig
Kristen Wiig is an American comedian, actress, and writer best known for her work on Saturday Night Live and films such as Bridesmaids.
-
C.
Maya Rudolph
Maya Rudolph is an American actress and comedian known for her work on "Saturday Night Live" and in numerous film and animated voice roles.
-
D.
Kathryn Hahn
Kathryn Hahn is an American actress and comedian known for her versatile roles in film and television, including prominent work in comedies and voice acting.
-
E.
Anna Faris
Anna Faris is an American actress and comedian best known for her lead role in the Scary Movie film series and her work in both film and television comedy.
- 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_69d6aafe02d881909900d54ad7d4af84 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a4654be881909bd0256cf18e25de |
completed | April 10, 2026, 7:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f01943196c8190900b46da238e1f24 |
completed | April 28, 2026, 2:19 a.m. |
Created at: April 8, 2026, 9:40 p.m.