Triple
T6249589
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bride Wars |
E140012
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Candice Bergen |
E76795
|
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: Candice Bergen | Statement: [Bride Wars, starring, Candice Bergen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Candice Bergen Context triple: [Bride Wars, starring, Candice Bergen]
-
A.
Candice Bergen
chosen
Candice Bergen is an American actress and former fashion model best known for her Emmy-winning role as the sharp-tongued journalist Murphy Brown on the hit television sitcom of the same name.
-
B.
Karen Kline
Karen Kline is an American psychotherapist best known as the longtime spouse of Academy Award–winning actress Linda Hunt.
-
C.
Alley Mills
Alley Mills is an American actress best known for her role as Norma Arnold, the mother on the classic television series "The Wonder Years."
-
D.
Nancy Allen
Nancy Allen is an American actress best known for her roles in films such as "Carrie," "Dressed to Kill," and the "RoboCop" series.
-
E.
Rhea Perlman
Rhea Perlman is an American actress best known for her Emmy-winning role as the sharp-tongued waitress Carla Tortelli on the classic sitcom "Cheers."
- 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_69c008b4858c819095b0199114a9a87b |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0633c5f2081909b0246e061f8a7d9 |
completed | March 22, 2026, 9:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c669d6ff748190b77d5a2c9cbe506b |
completed | March 27, 2026, 11:28 a.m. |
Created at: March 22, 2026, 4:24 p.m.