Triple
T14080021
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | How to Get Away with Murder |
E338840
|
entity |
| Predicate | starredActor |
P5563
|
FINISHED |
| Object | Liza Weil |
E633969
|
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: Liza Weil | Statement: [How to Get Away with Murder, starredActor, Liza Weil]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Liza Weil Context triple: [How to Get Away with Murder, starredActor, Liza Weil]
-
A.
Liza Weil
chosen
Liza Weil is an American actress best known for her roles as Paris Geller on "Gilmore Girls" and Bonnie Winterbottom on "How to Get Away with Murder."
-
B.
Liza Snyder
Liza Snyder is an American television actress best known for her comedic roles on sitcoms such as "Yes, Dear" and "Man with a Plan."
-
C.
Liz Gorinsky
Liz Gorinsky is an acclaimed science fiction and fantasy editor known for her influential work at Tor Books and for winning major genre awards.
-
D.
Shari Weiser
Shari Weiser is a puppeteer and performer best known for physically portraying the character Hoggle in Jim Henson’s fantasy film "Labyrinth."
-
E.
Rachel Leibowitz
Rachel Leibowitz is a person notable enough to be specifically cited as a bearer of the surname Leibowitz.
- 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_69d81c687b0c819087fd9ed4198403f8 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de5c5e027881908f610f5bab7598d4 |
completed | April 14, 2026, 3:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd8a9eeff48190b8aa9a601574395a |
completed | May 8, 2026, 7:02 a.m. |
Created at: April 9, 2026, 10:21 p.m.