Triple
T1065464
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Norwegian Armed Forces |
E23199
|
entity |
| Predicate | personnelStrength |
P24266
|
FINISHED |
| Object | around 25,000 active personnel (early 2020s) |
—
|
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: around 25,000 active personnel (early 2020s) | Statement: [Norwegian Armed Forces, personnelStrength, around 25,000 active personnel (early 2020s)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: personnelStrength Context triple: [Norwegian Armed Forces, personnelStrength, around 25,000 active personnel (early 2020s)]
-
A.
personnelComposition
Indicates the makeup or distribution of people or roles within a group, organization, or unit.
-
B.
combatantStrength
Indicates the relative level of power, capability, or effectiveness one combatant has in a conflict or confrontation compared to others.
-
C.
peakPersonnel
Indicates the maximum number of personnel involved or present at any point during a specified period or activity.
-
D.
personnelType
Indicates the classification or role category assigned to a person within an organization or system.
-
E.
garrisonSize
Indicates the number of troops or defenders stationed at a particular location as its garrison.
- 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_69a493ee1f908190992b5f0d1b04459b |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b9e1047481909af1cf8df2a01fff |
completed | March 1, 2026, 10:12 p.m. |
| PD | Predicate disambiguation | batch_69a4b736f1e881909bace735b38c0ade |
completed | March 1, 2026, 10:01 p.m. |
| PDg | Predicate description generation | batch_69a4b9df0a308190a0d87dcd8afe58bc |
completed | March 1, 2026, 10:12 p.m. |
Created at: March 1, 2026, 7:42 p.m.