Triple
T5235005
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Klute |
E118199
|
entity |
| Predicate | costumeDesignBy |
P184
|
FINISHED |
| Object | Ann Roth |
E429826
|
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: Ann Roth | Statement: [Klute, costumeDesignBy, Ann Roth]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ann Roth Context triple: [Klute, costumeDesignBy, Ann Roth]
-
A.
Ann Roth
chosen
Ann Roth is an acclaimed American costume designer known for her extensive work in film, theatre, and television, including multiple Academy Award–winning designs.
-
B.
Barbara Robbins
Barbara Robbins is known as the wife of Jon Lindbergh, the son of famed aviator Charles Lindbergh.
-
C.
Ann Rosener
Ann Rosener was an American photographer best known for her documentary images of home-front life and industry during World War II, particularly through her work for U.S. government agencies.
-
D.
Patricia Russo
Patricia Russo is an American business executive best known for serving as CEO of Lucent Technologies and later Alcatel-Lucent.
-
E.
Marion Rothman
Marion Rothman was an American film editor known for her work on notable films of the 1960s and 1970s, including the crime thriller "The Boston Strangler."
- 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_69bd4467db0881909b3b0982df32cc8f |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7b064b6881909f5746f55aa422c6 |
completed | March 20, 2026, 4:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf486fb3f48190a07829bcb9d0f521 |
completed | March 22, 2026, 1:39 a.m. |
Created at: March 20, 2026, 1:49 p.m.