Triple
T23103148
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jules Ostin |
E576086
|
entity |
| Predicate | relationshipToBenWhittaker |
P150928
|
FINISHED |
| Object | mentor-mentee and friendship |
—
|
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: mentor-mentee and friendship | Statement: [Jules Ostin, relationshipToBenWhittaker, mentor-mentee and friendship]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToBenWhittaker Context triple: [Jules Ostin, relationshipToBenWhittaker, mentor-mentee and friendship]
-
A.
relationshipToBenny
Indicates the specific type of personal or social relationship that an entity has with Benny.
-
B.
relationshipToTucker
Indicates the specific familial, social, or professional relationship that one entity has to Tucker.
-
C.
relationshipToJoeBuck
Indicates the specific familial, social, or professional relationship that one entity has to the person Joe Buck.
-
D.
hasRelationshipTypeWithBenBoykewich
Indicates that an entity has a specific type of relationship or connection with Ben Boykewich.
-
E.
relationshipToBoDecker
Indicates the specific type of personal or social relationship an entity has with Bo Decker.
- 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_69e245c060b48190a9bd61a47a16db17 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18deaacbc8190a97e64e1cf39cdd6 |
completed | April 29, 2026, 4:49 a.m. |
| PD | Predicate disambiguation | batch_69ef89e5ce748190b2c3ac3843484127 |
completed | April 27, 2026, 4:08 p.m. |
| PDg | Predicate description generation | batch_69ef9b7494f4819088ae59ea3d0ae8ab |
completed | April 27, 2026, 5:23 p.m. |
Created at: April 17, 2026, 3:58 p.m.