Triple
T25745106
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | L. Brent Bozell Jr. |
E648323
|
entity |
| Predicate | sibling-in-law |
P18076
|
FINISHED |
| Object | William F. Buckley Jr. |
—
|
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: William F. Buckley Jr. | Statement: [L. Brent Bozell Jr., sibling-in-law, William F. Buckley Jr.]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sibling-in-law Context triple: [L. Brent Bozell Jr., sibling-in-law, William F. Buckley Jr.]
-
A.
brotherInLaw
chosen
Indicates a relationship where one person is the brother of someone's spouse, the spouse of someone's sibling, or the spouse of the sibling of someone's spouse.
-
B.
spouseOfSiblingOf
Indicates the person who is married to someone’s sibling.
-
C.
inLaw
Indicates a familial relationship created through marriage, such as between a spouse and their partner’s relatives or between relatives of two spouses.
-
D.
siblingOrRelative
Indicates that two entities are related to each other by blood, marriage, or family ties, including but not limited to being siblings.
-
E.
sibling
Indicates that two entities share at least one parent, making them brothers or sisters to each other.
- F. None of above.
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_69e7ab306eec8190b05c312c6ab186b8 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f5fd1e9dd081908c70074c8aa49e51 |
completed | May 2, 2026, 1:33 p.m. |
| PD | Predicate disambiguation | batch_69f4938262ac8190b41f922d0407d272 |
completed | May 1, 2026, 11:50 a.m. |
Created at: April 22, 2026, 3:50 a.m.