Triple
T27077883
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | D.O. |
E685511
|
entity |
| Predicate | professionalEquivalentTo |
P165876
|
FINISHED |
| Object | M.D. in the United States |
—
|
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: M.D. in the United States | Statement: [D.O., professionalEquivalentTo, M.D. in the United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: professionalEquivalentTo Context triple: [D.O., professionalEquivalentTo, M.D. in the United States]
-
A.
professionalBase
Indicates that one entity serves as the primary professional location, organization, or base of operations for another entity.
-
B.
professionalStandardFor
Indicates that something defines, specifies, or serves as an authoritative professional standard that another entity is expected to follow or comply with.
-
C.
professionalCategory
Indicates the classification of an entity according to its professional field, role, or occupational domain.
-
D.
professionalWins
Indicates that one entity has achieved a certain number of victories or successes in a professional context, such as in a career, competition, or formal domain.
-
E.
professionalClass
Indicates that an entity belongs to, or is categorized within, a particular professional or occupational class.
- 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_69ef14843b1481909d828b3d5a44550a |
completed | April 27, 2026, 7:47 a.m. |
| NER | Named-entity recognition | batch_69f65b14512c8190a40e70319dcc54cd |
completed | May 2, 2026, 8:14 p.m. |
| PD | Predicate disambiguation | batch_69f659cc571c819097e51e531961d812 |
completed | May 2, 2026, 8:08 p.m. |
| PDg | Predicate description generation | batch_69f65a9cb0bc8190bf8a9b319900bad5 |
completed | May 2, 2026, 8:12 p.m. |
Created at: April 27, 2026, 8:32 a.m.