Triple
T25835792
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | F.D.R.: My Exploited Father-in-Law |
E650791
|
entity |
| Predicate | relationshipToSubjectOfBook |
P88403
|
FINISHED |
| Object | author was Roosevelt’s son-in-law |
—
|
LITERAL 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: author was Roosevelt’s son-in-law | Statement: [F.D.R.: My Exploited Father-in-Law, relationshipToSubjectOfBook, author was Roosevelt’s son-in-law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToSubjectOfBook Context triple: [F.D.R.: My Exploited Father-in-Law, relationshipToSubjectOfBook, author was Roosevelt’s son-in-law]
-
A.
relationshipToBooks
Indicates the nature or type of connection an entity has with one or more books, such as ownership, authorship, usage, or preference.
-
B.
hasAuthorRelationshipToSubject
Indicates that an entity serves as the author or creator of the specified subject.
-
C.
authorRelationshipToMainSubject
chosen
Indicates the nature of the connection or role the author has in relation to the main subject.
-
D.
subjectRelationToAuthor
Indicates the relationship or connection that the subject has to the author.
-
E.
subjectRelation
Indicates that one entity stands in a specified relational role or connection to another entity.
- 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_69e7ab37438081908f1ccf6284839520 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69fd49f6dbac81909744373a357b7982 |
completed | May 8, 2026, 2:27 a.m. |
| PD | Predicate disambiguation | batch_69fd48ed68f481908374183c66a6b055 |
completed | May 8, 2026, 2:22 a.m. |
Created at: April 22, 2026, 7:41 a.m.