Triple
T13597537
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Special Counsel investigation into Russian interference in the 2016 United States elections |
E324859
|
entity |
| Predicate | reportVolumeCount |
P2734
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Special Counsel investigation into Russian interference in the 2016 United States elections, reportVolumeCount, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: reportVolumeCount Context triple: [Special Counsel investigation into Russian interference in the 2016 United States elections, reportVolumeCount, 2]
-
A.
numberOfVolumes
chosen
Indicates the total count of separate volumes or parts that make up a multi-volume work or collection.
-
B.
collectionVolume
Indicates the total physical or spatial amount occupied by a collection, such as its size, capacity, or volume.
-
C.
volumeOf
Indicates the quantitative three-dimensional space occupied by an entity or contained within an object.
-
D.
vaultTrackCount
Indicates the number of tracks associated with or stored in a particular vault.
-
E.
volumeLevels
Indicates the relative loudness or intensity settings assigned to one or more entities or audio outputs.
- 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_69d80769eaf081909d82f44e484d6113 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb0590558819080ccc5874a650b1e |
completed | April 12, 2026, 2:46 p.m. |
| PD | Predicate disambiguation | batch_69dbae18eaf48190809e8b365856cde9 |
completed | April 12, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:49 p.m.