Triple
T2937037
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ACM SIGMETRICS Best Paper Award |
E79292
|
entity |
| Predicate | topicArea |
P28568
|
FINISHED |
| Object | computer networks performance |
—
|
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 networks performance | Statement: [ACM SIGMETRICS Best Paper Award, topicArea, computer networks performance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: topicArea Context triple: [ACM SIGMETRICS Best Paper Award, topicArea, computer networks performance]
-
A.
thematicArea
chosen
Indicates the subject or item is associated with, or falls under, a particular thematic area or topic of focus.
-
B.
primaryArea
Indicates that one entity is the main or most important area, domain, or field associated with another entity.
-
C.
competenceArea
Indicates that one entity has a particular domain, field, or area in which it possesses competence, expertise, or responsibility.
-
D.
primaryTopicOf
Indicates that a given subject is the main or central topic described by another resource (such as a document, page, or record).
-
E.
regionOfAcademicFocus
Indicates the academic subject area or discipline that an entity (such as a person or program) primarily concentrates on or specializes in.
- 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_69ad8b0fbab081908f6a61567c045d8d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad983f91c48190b409d8f522cab08b |
completed | March 8, 2026, 3:39 p.m. |
| PD | Predicate disambiguation | batch_69ad96088fb481909976b436c2b729d9 |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 2:56 p.m.