Triple
T10819401
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Don Beyer |
E255323
|
entity |
| Predicate | hasChild |
P369
|
FINISHED |
| Object | Don Beyer Jr. |
E255323
|
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: Don Beyer Jr. | Statement: [Don Beyer, hasChild, Don Beyer Jr.]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Don Beyer Jr. Context triple: [Don Beyer, hasChild, Don Beyer Jr.]
-
A.
Don Beyer
chosen
Don Beyer is an American Democratic politician and former Lieutenant Governor of Virginia who serves in the U.S. House of Representatives.
-
B.
Robert T. Beyer
Robert T. Beyer was an American physicist and translator known for bringing important scientific works, including foundational texts on quantum mechanics, to an English-speaking audience.
-
C.
Dion Beebe
Dion Beebe is an Academy Award–winning Australian–South African cinematographer known for his visually distinctive work on films such as "Memoirs of a Geisha" and "Collateral."
-
D.
Bill Bolling
Bill Bolling is an American Republican politician who served as the 39th Lieutenant Governor of Virginia from 2006 to 2014.
-
E.
Grant Bardsley
Grant Bardsley is a British voice actor best known for voicing the protagonist Taran in Disney’s animated film "The Black Cauldron."
- 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.