Triple
T1481082
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North Carolina College at Durham |
E30956
|
entity |
| Predicate | educationalLanguage |
P56
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [North Carolina College at Durham, educationalLanguage, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: educationalLanguage Context triple: [North Carolina College at Durham, educationalLanguage, English]
-
A.
educationalFocus
Indicates the primary subject area or theme that an educational activity, program, or resource is centered on.
-
B.
primaryLanguageOfInstruction
chosen
Indicates the language that is mainly used as the medium of teaching or instruction for a given educational context.
-
C.
educationalApproach
Indicates the method, strategy, or philosophy used to guide teaching and learning within an educational context.
-
D.
educationalModel
Indicates that one entity serves as an educational model, framework, or paradigm that guides or structures the teaching, learning, or training practices of another entity.
-
E.
educates
Indicates that one entity provides instruction, knowledge, or training to another entity.
- F. None of above.
Provenance (3 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_69a498fe55a88190ab7f9e40ace88e49 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c67699848190852e376efe22737c |
completed | March 1, 2026, 11:06 p.m. |
| PD | Predicate disambiguation | batch_69a4c484e52c81908948ff8c0a42751b |
completed | March 1, 2026, 10:58 p.m. |
Created at: March 1, 2026, 8:11 p.m.