Triple
T30799581
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daisy Suckley |
E784328
|
entity |
| Predicate | relationshipToFranklinDRoosevelt |
P204244
|
FINISHED |
| Object | distant cousin |
—
|
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: distant cousin | Statement: [Daisy Suckley, relationshipToFranklinDRoosevelt, distant cousin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToFranklinDRoosevelt Context triple: [Daisy Suckley, relationshipToFranklinDRoosevelt, distant cousin]
-
A.
relationshipToTruman
Indicates the specific familial, social, or professional connection that an entity has with Truman.
-
B.
relationshipTypeWithFrankUnderwood
Indicates the specific nature or category of relationship that an entity has with Frank Underwood.
-
C.
termRelationToPresident
Indicates the nature of a person’s connection or role in relation to a president, such as their position, association, or involvement with that president.
-
D.
relationshipToFrankDeFazio
Indicates the type or nature of a person's relationship to Frank DeFazio.
-
E.
relationshipToFrancis
Indicates the specific familial, social, or professional connection that an entity has with Francis.
- F. None of above. chosen
Provenance (4 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_69f224b3a7ec819096939414d103e31e |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_6a0356f541b08190b40469aa628087f4 |
completed | May 12, 2026, 4:36 p.m. |
| PD | Predicate disambiguation | batch_6a03569e4fa881909c57351a6c489636 |
completed | May 12, 2026, 4:34 p.m. |
| PDg | Predicate description generation | batch_6a0356f44b308190822c6becc71e3582 |
completed | May 12, 2026, 4:36 p.m. |
Created at: April 29, 2026, 8:42 p.m.