Triple
T25212483
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thelma Evans Anderson |
E631727
|
entity |
| Predicate | educationAspiration |
P159085
|
FINISHED |
| Object | college education |
—
|
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: college education | Statement: [Thelma Evans Anderson, educationAspiration, college education]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: educationAspiration Context triple: [Thelma Evans Anderson, educationAspiration, college education]
-
A.
educationIdeal
Indicates that something is regarded as the optimal or most desirable standard, goal, or model in the context of education.
-
B.
educationRight
Indicates that an entity holds a right or entitlement to receive education or educational opportunities.
-
C.
educationField
Indicates the academic or professional discipline in which an entity has been educated or trained.
-
D.
educationalField
Indicates the academic or disciplinary area in which an educational activity, program, or qualification is focused.
-
E.
educationType
Indicates the specific category or level of education associated with an entity, such as formal, informal, primary, secondary, or higher education.
- 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_69e75a8d1aa48190a4320acd3654762c |
completed | April 21, 2026, 11:07 a.m. |
| NER | Named-entity recognition | batch_69f497bc12b881908fe3386c66252bf6 |
completed | May 1, 2026, 12:08 p.m. |
| PD | Predicate disambiguation | batch_69f49366e8d08190adb4b71fe3a14683 |
completed | May 1, 2026, 11:49 a.m. |
| PDg | Predicate description generation | batch_69f497b8abb88190bb672cf6907c4b8d |
completed | May 1, 2026, 12:08 p.m. |
Created at: April 21, 2026, 12:58 p.m.