Triple
T31656050
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Philip J. Fry |
E807860
|
entity |
| Predicate | relationshipToProfessorHubertJFarnsworth |
P207482
|
FINISHED |
| Object | distant nephew |
—
|
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 nephew | Statement: [Philip J. Fry, relationshipToProfessorHubertJFarnsworth, distant nephew]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToProfessorHubertJFarnsworth Context triple: [Philip J. Fry, relationshipToProfessorHubertJFarnsworth, distant nephew]
-
A.
relationshipWithBurtVickerman
Indicates that there exists a specific interpersonal or professional connection between an entity and Burt Vickerman.
-
B.
relationshipToSheldonCooper
Indicates the specific interpersonal or familial connection that an entity has to Sheldon Cooper.
-
C.
relationshipToHomer
Indicates the specific familial or social relationship that one entity has to Homer.
-
D.
relationshipToFerris
Indicates the specific familial, social, or professional relationship that one entity has to Ferris.
-
E.
relationshipToBobBelcher
Indicates the specific familial, social, or professional relationship that one entity has to Bob Belcher.
- 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_69f348daf95c81908b4c985b7ddcd0b3 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_6a037c9141dc819098d7fcc36e69882c |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379e7aa0c8190bdc9ee4d54fc821b |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c7ee0388190a29faeb5cdb0950a |
completed | May 12, 2026, 7:16 p.m. |
Created at: April 30, 2026, 10:55 p.m.