Triple
T3630326
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anchorman 2: The Legend Continues |
E76938
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Meagan Good |
E65940
|
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: Meagan Good | Statement: [Anchorman 2: The Legend Continues, castMember, Meagan Good]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Meagan Good Context triple: [Anchorman 2: The Legend Continues, castMember, Meagan Good]
-
A.
Meagan Good
chosen
Meagan Good is an American actress known for her work in film and television, particularly in romantic comedies and dramas.
-
B.
Shannon Elizabeth
Shannon Elizabeth is an American actress and former fashion model best known for her breakout role in the comedy film "American Pie."
-
C.
Lauren Jones
Lauren Jones is an American model, actress, and television personality known for her appearances in film, TV, and professional wrestling.
-
D.
Tara Reid
Tara Reid is an American actress best known for her roles in films like "American Pie" and "The Big Lebowski," as well as the "Sharknado" television movie series.
-
E.
Joely Richardson
Joely Richardson is an English actress known for her work in film and television, including roles in projects such as "Nip/Tuck" and various period dramas.
- 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_69ad85dc03948190b35b7189e4175bcc |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc300223881909019982ebf194f78 |
completed | March 8, 2026, 6:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b44f12c2608190bd0eb25c9df97ec6 |
completed | March 13, 2026, 5:53 p.m. |
Created at: March 8, 2026, 3:23 p.m.