Triple
T15737581
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bob Wiley |
E381512
|
entity |
| Predicate | relationshipToDrLeoMarvin |
P120415
|
FINISHED |
| Object | 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: patient | Statement: [Bob Wiley, relationshipToDrLeoMarvin, patient]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToDrLeoMarvin Context triple: [Bob Wiley, relationshipToDrLeoMarvin, patient]
-
A.
relationshipToDoctor
Indicates the specific personal or professional connection an individual has with a doctor (e.g., self, spouse, parent, guardian, colleague).
-
B.
relationshipWithDoctorParnassus
Indicates that an entity has a personal or professional relationship with Doctor Parnassus.
-
C.
relationshipToLloyd
Indicates the specific type of personal or social relationship an entity has with Lloyd.
-
D.
relationshipToDrWatson
Indicates the specific personal or professional relationship an entity has with Dr. Watson.
-
E.
relationshipToLaurie
Indicates the specific type of relationship or connection that an entity has to Laurie.
- 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_69d86d9cdb648190bf3171be0bd7d872 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e0b4d6b5788190883746ee82c799f5 |
completed | April 16, 2026, 10:07 a.m. |
| PD | Predicate disambiguation | batch_69e0052c6208819098165d61d378d13b |
completed | April 15, 2026, 9:37 p.m. |
| PDg | Predicate description generation | batch_69e0b4d01c9c81909f6b611e8144c838 |
completed | April 16, 2026, 10:07 a.m. |
Created at: April 10, 2026, 4:46 a.m.