Triple
T11995336
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tobias "Toby" Wolff |
E285513
|
entity |
| Predicate | educationGoal |
P102783
|
FINISHED |
| Object | to attend an East Coast prep school |
—
|
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: to attend an East Coast prep school | Statement: [Tobias "Toby" Wolff, educationGoal, to attend an East Coast prep school]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: educationGoal Context triple: [Tobias "Toby" Wolff, educationGoal, to attend an East Coast prep school]
-
A.
educationalObjective
Indicates the intended learning goal, skill, or competency that an educational resource, activity, or program is designed to achieve.
-
B.
educationalFocus
Indicates the primary subject area or theme that an educational activity, program, or resource is centered on.
-
C.
educationSetting
Indicates the type or context of the educational environment in which the related entity participates or operates.
-
D.
seeksEducationFrom
Indicates that one entity pursues learning, training, or academic instruction from another entity as a source of education.
-
E.
educationIndicator
Indicates that there is a measure or metric reflecting some aspect of educational status, performance, or outcomes associated with the entities.
- 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_69d6ab44a77c8190a652f4b27164e4ef |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d903b211688190bfe6dd15c3f96d2f |
completed | April 10, 2026, 2:05 p.m. |
| PD | Predicate disambiguation | batch_69d902abca70819098291aa51b593708 |
completed | April 10, 2026, 2:01 p.m. |
| PDg | Predicate description generation | batch_69d903a8695c8190bfa9d7ca50834f9f |
completed | April 10, 2026, 2:05 p.m. |
Created at: April 8, 2026, 9:46 p.m.