Triple
T6067529
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deep Impact |
E135196
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object | Téa Leoni |
E289044
|
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: Téa Leoni | Statement: [Deep Impact, stars, Téa Leoni]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Téa Leoni Context triple: [Deep Impact, stars, Téa Leoni]
-
A.
Téa Leoni
chosen
Téa Leoni is an American actress and producer best known for her leading roles in film and television, including the political drama series "Madam Secretary."
-
B.
Elizabeth Berkley
Elizabeth Berkley is an American actress best known for her roles in the TV series "Saved by the Bell" and the film "Showgirls."
-
C.
Annabella Sciorra
Annabella Sciorra is an American actress known for her work in film and television, including acclaimed roles in movies like "Jungle Fever" and the TV series "The Sopranos."
-
D.
Virginia Madsen
Virginia Madsen is an American actress known for her versatile film and television roles, including acclaimed performances in movies such as "Sideways" and "Candyman."
-
E.
Tyne Daly
Tyne Daly is an American actress acclaimed for her powerful performances in television dramas, film, and theater, including her iconic role in the series "Cagney & Lacey."
- 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_69c00879e8048190b690717d19c5bc03 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c057403a8081908b593472fcc0d699 |
completed | March 22, 2026, 8:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c1414ea324819087dc9267938efb5c |
completed | March 23, 2026, 1:34 p.m. |
Created at: March 22, 2026, 4:10 p.m.