Triple
T17376014
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United Nations–African Union Mission in Darfur |
E422439
|
entity |
| Predicate | troopContributingCountriesCount |
P2436
|
FINISHED |
| Object | more than 30 |
—
|
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: more than 30 | Statement: [United Nations–African Union Mission in Darfur, troopContributingCountriesCount, more than 30]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: troopContributingCountriesCount Context triple: [United Nations–African Union Mission in Darfur, troopContributingCountriesCount, more than 30]
-
A.
numberOfParticipatingNations
chosen
Indicates the total count of nations that take part in a specified event, activity, or context.
-
B.
numberOfTroopsInvolved
Indicates the quantity of military personnel participating in or assigned to a specific operation, event, or engagement.
-
C.
countryRepresentedCount
Indicates the number of distinct countries that are represented or associated with a given entity.
-
D.
hasContributingCountry
Indicates that a country has contributed resources, support, or participation to the subject entity or activity.
-
E.
countryOfUSForces
Indicates that a given country is the nation in which United States military forces are present or operating.
- 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_69d889d6535c81908be333c01deaec4e |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a6c864481908507290282cc6d25 |
completed | April 19, 2026, 2:14 a.m. |
| PD | Predicate disambiguation | batch_69e3b02ac8688190a7182f1b2151d721 |
completed | April 18, 2026, 4:24 p.m. |
Created at: April 10, 2026, 5:45 a.m.