Triple
T2129729
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Barely Lethal |
E46508
|
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: [Barely Lethal, editedBy, Lisa Lassek]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lisa Lassek Context triple: [Barely Lethal, 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.
Julie Naschauer
Julie Naschauer was the wife of Theodor Herzl, the Austro-Hungarian journalist and founder of modern political Zionism.
-
C.
Lori Collins
Lori Collins is a central character in the comedy film "Ted," known as John Bennett’s long-suffering girlfriend who pushes him to grow up and choose between her and his crude, living teddy bear best friend.
-
D.
Amy Eshleman
Amy Eshleman is an American former public librarian and education advocate best known as the wife of former Chicago mayor Lori Lightfoot.
-
E.
Stefanie Ehrlich
Stefanie Ehrlich is known as a child of the prominent American biologist and author Paul Ehrlich.
- 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_69a88a1626548190ae59a5028c3baa8e |
completed | March 4, 2026, 7:37 p.m. |
| NER | Named-entity recognition | batch_69abbb77ccc4819087bee5dbb91b5ae8 |
completed | March 7, 2026, 5:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b367d99b548190981f471e167198da |
completed | March 13, 2026, 1:26 a.m. |
Created at: March 4, 2026, 7:44 p.m.