Triple
T13131116
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roman medicine |
E311967
|
entity |
| Predicate | hasKeyPractitionerType |
P108225
|
FINISHED |
| Object | physician |
—
|
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: physician | Statement: [Roman medicine, hasKeyPractitionerType, physician]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasKeyPractitionerType Context triple: [Roman medicine, hasKeyPractitionerType, physician]
-
A.
hasKeyInstitutionType
Indicates that an entity is associated with a specific type or category of key institution.
-
B.
hasKeyType
Indicates that an entity possesses or is associated with a specific category or type of key.
-
C.
hasMemberType
Indicates that an entity includes or is associated with members belonging to a specified type or category.
-
D.
hasStakeholderType
Indicates that an entity is associated with a stakeholder and specifies the category or role that stakeholder fulfills in relation to the entity.
-
E.
hasAffiliationType
Indicates that one entity is connected to another through a specified kind or category of affiliation or association.
- 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_69d806a9fe888190b081e2d9ea665d6c |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d981b27a8c81909a92ab7be5d3a7e9 |
completed | April 10, 2026, 11:03 p.m. |
| PD | Predicate disambiguation | batch_69d98043a74c81908648e6cd0b4c7f71 |
completed | April 10, 2026, 10:57 p.m. |
| PDg | Predicate description generation | batch_69d98134df64819084a5674f9475dcc2 |
completed | April 10, 2026, 11:01 p.m. |
Created at: April 9, 2026, 9:07 p.m.