Triple
T13153192
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hut 6 |
E312515
|
entity |
| Predicate | approximateStaffSize |
P17907
|
FINISHED |
| Object | several hundred personnel |
—
|
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: several hundred personnel | Statement: [Hut 6, approximateStaffSize, several hundred personnel]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateStaffSize Context triple: [Hut 6, approximateStaffSize, several hundred personnel]
-
A.
employsApproximateNumberOfPeople
chosen
Indicates that an entity employs a roughly estimated or approximate number of people, rather than an exact headcount.
-
B.
staffSize
Indicates the number of staff members associated with an entity.
-
C.
estimatedMemberCount
Indicates the approximate or predicted number of members associated with an entity.
-
D.
approximateAudienceSize
Indicates an estimated number of individuals or entities that are expected to be reached or affected in a given context.
-
E.
typicalTeamSize
Indicates the usual or most common number of members that make up a given team.
- 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_69d806aabde48190899e13e41659cae5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98cf054f88190b05ced98d5a22a62 |
completed | April 10, 2026, 11:51 p.m. |
| PD | Predicate disambiguation | batch_69d98bbd1d088190b7c69f37fc6eeb64 |
completed | April 10, 2026, 11:46 p.m. |
Created at: April 9, 2026, 9:11 p.m.