Triple
T866747
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jeff Jarvis |
E18718
|
entity |
| Predicate | knownFor |
P22
|
FINISHED |
| Object | BuzzMachine blog |
E100835
|
NE 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: BuzzMachine blog | Statement: [Jeff Jarvis, knownFor, BuzzMachine blog]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: BuzzMachine blog Context triple: [Jeff Jarvis, knownFor, BuzzMachine blog]
-
A.
BuzzMachine
chosen
BuzzMachine is the media and journalism-focused blog written by professor and author Jeff Jarvis, known for commentary on the future of news and digital media.
-
B.
Boing Boing
Boing Boing is a long-running, influential blog and online magazine that covers technology, culture, science fiction, and digital rights with a quirky, countercultural tone.
-
C.
Engadget
Engadget is a technology news and reviews website that covers consumer electronics, gadgets, and digital culture.
-
D.
Technium
Technium is an exhibition floor at Amsterdam's NEMO Science Museum that showcases interactive science and technology displays for visitors.
-
E.
Autoblog
Autoblog is an automotive news and review website known for its coverage of car industry news, vehicle reviews, and consumer car-buying information.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69a4938ce8688190a24bdfef82ba7d21 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ac7cb1888190a46c16b30256451b |
completed | March 1, 2026, 9:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7b84dcf748190b20372fdc48d6766 |
completed | March 4, 2026, 4:42 a.m. |
Created at: March 1, 2026, 7:39 p.m.