Triple
T19802034
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rebecca Hutman |
E475704
|
entity |
| Predicate | hasRelationship |
P37
|
FINISHED |
| Object | Larry Daley |
—
|
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: Larry Daley | Statement: [Rebecca Hutman, hasRelationship, Larry Daley]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Larry Daley Context triple: [Rebecca Hutman, hasRelationship, Larry Daley]
-
A.
Larry Daley
chosen
Larry Daley is the bumbling yet good-hearted night guard protagonist of the "Night at the Museum" film series, known for dealing with museum exhibits that magically come to life.
-
B.
Brad Daugherty
Brad Daugherty is a former NBA center best known for his All-Star career with the Cleveland Cavaliers in the late 1980s and early 1990s.
-
C.
Darryl Philbin
Darryl Philbin is a laid-back yet sharp-witted warehouse foreman who becomes a key supporting character and later office employee in the U.S. version of The Office.
-
D.
Kevin Daley
Kevin Daley is one of the children of the late Chicago First Lady Maggie Daley and former Mayor Richard M. Daley.
-
E.
Billy Daugherty
Billy Daugherty is an individual notable enough to be recognized as a prominent bearer of the surname Daugherty.
- 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_69d8e51bc4208190a1c57d8c5d1b15e4 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e654257cb4819096fb2aa5d1f7fbb0 |
completed | April 20, 2026, 4:28 p.m. |
Created at: April 10, 2026, 1:49 p.m.