Triple
T28566242
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Donald Mallard |
E722684
|
entity |
| Predicate | educationInBackstory |
P171792
|
FINISHED |
| Object | medical degree |
—
|
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: medical degree | Statement: [Donald Mallard, educationInBackstory, medical degree]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: educationInBackstory Context triple: [Donald Mallard, educationInBackstory, medical degree]
-
A.
educationHistory
Indicates that an entity has a record of formal learning experiences or academic qualifications associated with it.
-
B.
historicalBackground
Indicates that one entity provides contextual historical information or circumstances that help explain the origin, development, or significance of another entity.
-
C.
educationAspiration
Indicates an individual's intended or desired level of education or educational achievement.
-
D.
educatedIn
Indicates that an entity received education or formal training at a specified institution or place.
-
E.
educationInRealIdentity
Indicates that an entity’s educational background or activities are associated with their real-world, non-pseudonymous identity.
- 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_69f01a5f69d08190ad5c0d2167078dec |
completed | April 28, 2026, 2:24 a.m. |
| NER | Named-entity recognition | batch_69f6a28c7c148190bfc980aad9f678ca |
completed | May 3, 2026, 1:19 a.m. |
| PD | Predicate disambiguation | batch_69f69fe1e3c88190830bb2e9f407357e |
completed | May 3, 2026, 1:07 a.m. |
| PDg | Predicate description generation | batch_69f6a28b8ea881908733485374771c51 |
completed | May 3, 2026, 1:19 a.m. |
Created at: April 28, 2026, 4:07 a.m.