Triple
T22977271
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Allison Becker |
E571359
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Laird Becker |
—
|
NE NERFINISHED |
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: Laird Becker | Statement: [Allison Becker, spouse, Laird Becker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laird Becker Context triple: [Allison Becker, spouse, Laird Becker]
-
A.
Laird Becker
chosen
Laird Becker is a supporting character in the 2023 comedy film "No Hard Feelings," involved in the story’s awkward and humorous romantic entanglements.
-
B.
Mark Dean
Mark Dean is an American computer engineer and inventor best known for his pioneering work on the IBM personal computer and early PC architecture.
-
C.
Leonard Bosack
Leonard Bosack is an American computer engineer and entrepreneur best known as the co-founder of Cisco Systems, a pioneering company in computer networking and internet infrastructure.
-
D.
Steve Symms
Steve Symms is a Republican politician who represented Idaho in the U.S. Senate during the 1980s and early 1990s.
-
E.
Pat Proft
Pat Proft is an American comedy writer and screenwriter best known for his work on spoof film franchises such as The Naked Gun and Police Academy.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245b3c50481908bb3741ec9f40862 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18292f3788190ab4e9d559e0070c8 |
completed | April 29, 2026, 4:01 a.m. |
Created at: April 17, 2026, 3:48 p.m.