Triple
T7943547
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Jam |
E184444
|
entity |
| Predicate | influenced |
P9
|
FINISHED |
| Object | Blur |
E400752
|
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: Blur | Statement: [The Jam, influenced, Blur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Blur Context triple: [The Jam, influenced, Blur]
-
A.
Blur
chosen
Blur is an English rock band central to the 1990s Britpop movement, known for their eclectic sound and socially observant lyrics.
-
B.
Circle of Confusion
Circle of Confusion is a management and production company known for developing and producing genre-focused film and television projects, including the hit series "The Walking Dead."
-
C.
Karmir Blur
Karmir Blur is an important archaeological site in Armenia containing the remains of an ancient Urartian fortress and settlement.
-
D.
Bokeh
Bokeh is an interactive visualization library for Python that enables the creation of rich, web-ready plots and dashboards from large or streaming datasets.
-
E.
Photo Unblur
Photo Unblur is a Google Photos feature that uses AI to sharpen and clarify blurry images, improving their overall quality and detail.
- 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_69ca8291c2008190b1b8832c87814bcf |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3b0d31548190af54a2f3bbb8cad3 |
completed | March 31, 2026, 3:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc5650cfb08190846e040f85c8369d |
completed | March 31, 2026, 11:18 p.m. |
Created at: March 30, 2026, 5:09 p.m.