Triple
T10819399
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Don Beyer |
E255323
|
entity |
| Predicate | hasChild |
P369
|
FINISHED |
| Object | Grace Beyer |
E887778
|
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: Grace Beyer | Statement: [Don Beyer, hasChild, Grace Beyer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Grace Beyer Context triple: [Don Beyer, hasChild, Grace Beyer]
-
A.
Caroline Becker
Caroline Becker is a key resistance leader character in the Wolfenstein video game series, known for organizing and directing the fight against the Nazi regime.
-
B.
Grace Byers
Grace Byers is an American actress best known for her role as Anika Calhoun on the television series "Empire."
-
C.
Kirsten Beyer
Kirsten Beyer is an American author and television writer best known for her work on Star Trek novels and for helping develop and write modern Star Trek series such as Star Trek: Discovery and Star Trek: Picard.
-
D.
Megan Beyer
chosen
Megan Beyer is an American journalist and civic leader known for her work in cultural diplomacy, gender equality, and public policy initiatives.
-
E.
Anne Schaefer
Anne Schaefer was an American silent film actress active in the early 20th century, appearing in numerous productions during the 1910s and 1920s.
- 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_69d6aa8081448190a9324184f2bd1c26 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d734492be88190874ea0ba4d0fa643 |
completed | April 9, 2026, 5:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69deb0f9e3a081908163b398d845deeb |
completed | April 14, 2026, 9:26 p.m. |
Created at: April 8, 2026, 9:18 p.m.