Triple
T22840589
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Unbelievable |
E566067
|
entity |
| Predicate | executiveProducer |
P7225
|
FINISHED |
| Object | Sarah Timberman |
—
|
NE NERFINISHED |
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: [Unbelievable, executiveProducer, Sarah Timberman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sarah Timberman Context triple: [Unbelievable, executiveProducer, Sarah Timberman]
-
A.
Sarah Timberman
chosen
Sarah Timberman is an American television producer known for her work on numerous acclaimed drama series.
-
B.
Julia Tillman
Julia Tillman is a vocalist and musician known for her work featured on the album "Tapestry."
-
C.
Katie DeWitt
Katie DeWitt is a person notable enough to be recognized as a prominent bearer of the De Witt surname.
-
D.
Susannah Flood
Susannah Flood is an American actress best known for her role as public defender Kate Littlejohn on the legal drama series "For the People."
-
E.
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.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245869e188190a196584f36e682da |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17e83fa48819084568264ef45c833 |
completed | April 29, 2026, 3:44 a.m. |
Created at: April 17, 2026, 3:35 p.m.