Triple
T15033629
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr. Nick |
E378420
|
entity |
| Predicate | relationshipWithDoctorParnassus |
P116466
|
FINISHED |
| Object | rival |
—
|
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: rival | Statement: [Mr. Nick, relationshipWithDoctorParnassus, rival]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipWithDoctorParnassus Context triple: [Mr. Nick, relationshipWithDoctorParnassus, rival]
-
A.
relationshipToDoctor
Indicates the specific personal or professional connection an individual has with a doctor (e.g., self, spouse, parent, guardian, colleague).
-
B.
patientRelationship
Indicates that one entity is the patient or recipient of an action, treatment, or service performed by another entity.
-
C.
medicalAffiliation
Indicates a formal professional or institutional relationship between an entity and a medical organization, such as employment, membership, or clinical association.
-
D.
hasDoctorActor
Indicates that a doctor participates as an acting agent in the specified event or relationship.
-
E.
associatedWithPractice
Indicates a relationship in which an entity is connected or linked to a particular practice, activity, or customary way of doing something.
- 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_69d85cd46b2c819090d054c27787f677 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded7e3a7c8819081f26c2435c1bcb2 |
completed | April 15, 2026, 12:12 a.m. |
| PD | Predicate disambiguation | batch_69de9a67cbc481909c19c2de57de4eb7 |
completed | April 14, 2026, 7:50 p.m. |
| PDg | Predicate description generation | batch_69deb1a88d588190996afa8e5b32b552 |
completed | April 14, 2026, 9:29 p.m. |
Created at: April 10, 2026, 2:59 a.m.