Triple
T19703284
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BarCamp |
E473148
|
entity |
| Predicate | typicalTopicsInclude |
P24066
|
FINISHED |
| Object | technology |
—
|
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: technology | Statement: [BarCamp, typicalTopicsInclude, technology]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalTopicsInclude Context triple: [BarCamp, typicalTopicsInclude, technology]
-
A.
typicalCourseTopic
Indicates that a given topic is commonly or characteristically covered as part of a particular course.
-
B.
coveredTopics
Indicates that certain subjects or themes have been addressed or included within a discussion, document, or activity.
-
C.
includesTopics
chosen
Indicates that one entity contains, covers, or addresses the specified topics as part of its content or scope.
-
D.
topicOfReports
Indicates that something serves as the main subject or focus of one or more reports.
-
E.
interviewTopic
Indicates that a particular subject or theme is the focus of discussion during an interview.
- 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_69d8e516dd048190a0b6c93ea3e71f58 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e642b8707081908fbf96c989d2d52d |
completed | April 20, 2026, 3:14 p.m. |
| PD | Predicate disambiguation | batch_69e530438c60819082364c7be3eef6f0 |
completed | April 19, 2026, 7:42 p.m. |
Created at: April 10, 2026, 1:46 p.m.