Triple
T13174755
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Serenity |
E313070
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Lisa Lassek |
E338425
|
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: Lisa Lassek | Statement: [Serenity, editedBy, Lisa Lassek]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lisa Lassek Context triple: [Serenity, editedBy, Lisa Lassek]
-
A.
Lisa Lassek
chosen
Lisa Lassek is an American film and television editor known for her frequent collaborations with Joss Whedon on projects such as major Marvel superhero films and cult TV series.
-
B.
Lisa Eilbacher
Lisa Eilbacher is an American actress best known for her roles in 1980s films and television series, including prominent appearances in action and drama movies.
-
C.
Denise Lakofski
Denise Lakofski, better known as Denise Scott Brown, is a pioneering architect, urban planner, and theorist whose work and writings have profoundly influenced postmodern architecture and urban design.
-
D.
Lisa Gottsegen
Lisa Gottsegen is an American businesswoman and philanthropist best known as the longtime wife of actor Dustin Hoffman.
-
E.
Laura Rister
Laura Rister is a film producer and executive known for her work on independent and prestige projects, including the financial thriller "Margin Call."
- 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_69d806ac3ee081909b2fd27d060aa974 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98c303e3c819086cf0f0b6d9e61ca |
completed | April 10, 2026, 11:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcd084d94081909ae911fce5640aaf |
completed | May 7, 2026, 5:48 p.m. |
Created at: April 9, 2026, 9:14 p.m.