Triple
T34018148
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Peterson |
E872303
|
entity |
| Predicate | hasOnlineCoverage |
P186986
|
FINISHED |
| Object | true-crime blogs |
—
|
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: true-crime blogs | Statement: [Michael Peterson, hasOnlineCoverage, true-crime blogs]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOnlineCoverage Context triple: [Michael Peterson, hasOnlineCoverage, true-crime blogs]
-
A.
hasCoverage
Indicates that one entity provides insurance or protection coverage for another entity or subject.
-
B.
hasAreaOfCoverage
Indicates that an entity provides services, influence, or applicability within a specified geographic or conceptual region.
-
C.
providesCoverage
Indicates that one entity supplies protection, insurance, or service coverage to another entity or for a specified risk or scope.
-
D.
hasCoverageRestriction
Indicates that there is a limitation, exclusion, or condition applied to the scope or extent of coverage provided.
-
E.
hasFrequencyCoverage
Indicates that one entity provides, supports, or is applicable across a specified range or set of frequencies associated with another entity.
- F. None of above. chosen
Provenance (4 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_69f349a19ad88190ab586f010c804a8f |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69fb2e940d5c8190bceae77daf4ef512 |
completed | May 6, 2026, 12:05 p.m. |
| PD | Predicate disambiguation | batch_69f9fec70bd881909c658a3c5020318b |
completed | May 5, 2026, 2:29 p.m. |
| PDg | Predicate description generation | batch_69fb2e9309fc81909dfefd9020d6fbad |
completed | May 6, 2026, 12:05 p.m. |
Created at: May 1, 2026, 1:51 a.m.