Triple
T15751945
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tower Heist |
E381867
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Tea 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: Tea Leoni | Statement: [Tower Heist, starring, Tea Leoni]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tea Leoni Context triple: [Tower Heist, starring, Tea 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.
Alice Patten
Alice Patten is a British actress best known internationally for her role as an English documentary filmmaker in the acclaimed Indian film "Rang De Basanti."
-
C.
Rebecca Washington
Rebecca Washington is a central character on the legal drama series "The Practice," known for her work as a dedicated attorney at the show's featured law firm.
-
D.
Claudia Christian
Claudia Christian is an American actress best known for her role as Commander Susan Ivanova on the science fiction television series "Babylon 5."
-
E.
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."
- 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_69d86d9e6b44819085d1f6a969ecb74c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e05030e31081908c307a8dc7067db4 |
completed | April 16, 2026, 2:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00456b590c8190949fd23cb5cec1e8 |
completed | May 10, 2026, 8:44 a.m. |
Created at: April 10, 2026, 4:47 a.m.