Triple
T22269759
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mark Fisher |
E550442
|
entity |
| Predicate | blog |
P1205
|
FINISHED |
| Object | k-punk |
—
|
NE NERFINISHED |
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: k-punk | Statement: [Mark Fisher, blog, k-punk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: k-punk Context triple: [Mark Fisher, blog, k-punk]
-
A.
k-punk (blog)
chosen
k-punk (blog) was Mark Fisher’s influential online platform for cultural criticism, political theory, and hauntological reflections that helped define 2000s leftist and pop-cultural discourse.
-
B.
KP
KP is the nickname of Kirk Penney, a New Zealand former professional basketball player known for his prolific scoring and international career.
-
C.
KP
KP is the commonly used abbreviation for Khyber Pakhtunkhwa, a province in northwestern Pakistan known for its mountainous terrain and diverse ethnic communities.
-
D.
KP
KP is a subsystem of axiomatic set theory that omits the power set axiom and focuses on sets that are constructible via definable operations.
-
E.
KP
KP is the vehicle registration code used for the Sežana area in Slovenia.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e43d8208190aff4f9cf7f2c2a8a |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f141bf53a481909e2c79de4144f332 |
completed | April 28, 2026, 11:24 p.m. |
Created at: April 16, 2026, 8:40 p.m.