Triple
T9984084
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Justified |
E196521
|
entity |
| Predicate | executiveProducer |
P7225
|
FINISHED |
| Object | Sarah Timberman |
E402942
|
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: Sarah Timberman | Statement: [Justified, executiveProducer, Sarah Timberman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sarah Timberman Context triple: [Justified, executiveProducer, Sarah Timberman]
-
A.
Sarah Timberman
chosen
Sarah Timberman is an American television producer known for her work on numerous acclaimed drama series.
-
B.
Katie DeWitt
Katie DeWitt is a person notable enough to be recognized as a prominent bearer of the De Witt surname.
-
C.
Jennie Gerhardt
Jennie Gerhardt is a naturalist novel by American author Theodore Dreiser that portrays the struggles of a poor young woman entangled in class, morality, and social injustice in late 19th-century America.
-
D.
Susannah Shipman
Susannah Shipman is a film producer best known for her work on the Academy Award–winning documentary "Taxi to the Dark Side."
-
E.
Elizabeth Logue
Elizabeth Logue is an American actress best known for her work in film and television during the mid-20th century.
- 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_69ca82efbce081908179b4b9c65096eb |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb8bdc0388190bbbd4bdc5ac3adec |
completed | April 2, 2026, 12:30 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d257fe0e348190b55fbd38e21cff7c |
completed | April 5, 2026, 12:39 p.m. |
Created at: March 30, 2026, 8:49 p.m.