Triple
T8399420
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | computer graphics |
E198134
|
entity |
| Predicate | fieldOf |
P3
|
FINISHED |
| Object | computer science |
—
|
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: computer science | Statement: [computer graphics, fieldOf, computer science]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fieldOf Context triple: [computer graphics, fieldOf, computer science]
-
A.
fieldOfSignificance
Indicates that something holds particular importance, relevance, or impact within a specified domain, context, or area of interest.
-
B.
fieldOfWork
chosen
Indicates the professional or academic domain in which an entity is primarily engaged or specializes.
-
C.
fieldOfPublication
Indicates the academic or topical area in which a work is published.
-
D.
fieldOfRecognition
Indicates the domain, discipline, or area in which an entity is formally acknowledged, honored, or recognized.
-
E.
theorizedInField
Indicates that a theory, idea, or hypothesis was formulated or developed within a particular academic or scientific field.
- 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_69ca82f816bc8190ab321c07d72208c1 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb824ac29481909f02cdb2b7cd18af |
completed | March 31, 2026, 8:14 a.m. |
| PD | Predicate disambiguation | batch_69cb70d24b248190a326aa6804f942b5 |
completed | March 31, 2026, 6:59 a.m. |
Created at: March 30, 2026, 6:04 p.m.