Triple
T15709551
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Richard Dreyfuss as Dr. Leo Marvin |
E380802
|
entity |
| Predicate | relationshipToBobWiley |
P119876
|
FINISHED |
| Object | psychiatrist–patient |
—
|
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: psychiatrist–patient | Statement: [Richard Dreyfuss as Dr. Leo Marvin, relationshipToBobWiley, psychiatrist–patient]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToBobWiley Context triple: [Richard Dreyfuss as Dr. Leo Marvin, relationshipToBobWiley, psychiatrist–patient]
-
A.
relationshipToWoody
Indicates the type or nature of a subject’s relationship or connection to the entity Woody.
-
B.
relationshipToBillyBackus
Indicates the specific familial or social relationship that an entity has to the person named Billy Backus.
-
C.
relationshipToJoeBuck
Indicates the specific familial, social, or professional relationship that one entity has to the person Joe Buck.
-
D.
relationshipWithTomWambsgans
Indicates the existence and nature of a relationship or connection that an entity has with Tom Wambsgans.
-
E.
relationshipToTheDude
Indicates the specific type of personal or social relationship that one entity has to the individual referred to as "the Dude."
- 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_69d86d9bf930819082b30cf6d169297c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e04f8d8b648190842c635f2ae7bfa4 |
completed | April 16, 2026, 2:55 a.m. |
| PD | Predicate disambiguation | batch_69e00526759c819088b80d85138b8974 |
completed | April 15, 2026, 9:37 p.m. |
| PDg | Predicate description generation | batch_69e0094af5b481908ad51d5d7ba0c726 |
completed | April 15, 2026, 9:55 p.m. |
Created at: April 10, 2026, 4:45 a.m.