Triple
T1591642
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Resolute Support Mission |
E34189
|
entity |
| Predicate | numberOfContributingCountries |
P2436
|
FINISHED |
| Object | around 40 |
—
|
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 40 | Statement: [Resolute Support Mission, numberOfContributingCountries, around 40]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfContributingCountries Context triple: [Resolute Support Mission, numberOfContributingCountries, around 40]
-
A.
hasNumberOfCountries
Indicates the relationship that specifies how many countries are associated with or contained within a given entity.
-
B.
numberOfParticipatingNations
chosen
Indicates the total count of nations that take part in a specified event, activity, or context.
-
C.
associatedConfederationCount
Indicates the number of distinct confederations with which an entity is associated.
-
D.
benefitedCountry
Indicates that one country gains an advantage, profit, or positive outcome from an action, event, or entity associated with another.
-
E.
involvedCountry
Indicates that a country participates in, is associated with, or is otherwise implicated in the referenced event, activity, or situation.
- 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_69a885fceb2c8190b47e0f7c0aefbff0 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a93aedd45c819085843ac843d640e8 |
completed | March 5, 2026, 8:12 a.m. |
| PD | Predicate disambiguation | batch_69a907bdc19081908c84c5c0aa09e282 |
completed | March 5, 2026, 4:34 a.m. |
Created at: March 4, 2026, 7:27 p.m.