Triple
T351858
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United Nations Economic and Social Commission for Asia and the Pacific |
E7459
|
entity |
| Predicate | numberOfAssociateMembers |
P12132
|
FINISHED |
| Object | 9 |
—
|
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: 9 | Statement: [United Nations Economic and Social Commission for Asia and the Pacific, numberOfAssociateMembers, 9]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfAssociateMembers Context triple: [United Nations Economic and Social Commission for Asia and the Pacific, numberOfAssociateMembers, 9]
-
A.
numberOfBoardMembers
Indicates the total count of individuals who serve as members on a board.
-
B.
numberOfKnownMembers
Indicates the count of members within a group or set whose identities are known or have been explicitly determined.
-
C.
originalNumberOfMembers
Indicates the initial total count of members in a group or organization before any changes such as additions or removals.
-
D.
hasNumberOfMemberInstitutions
Indicates the quantitative count of member institutions associated with a given entity.
-
E.
numberOfAssemblyMembers
Indicates the total count of members that belong to a particular assembly.
- 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_69a2e7e696948190bebc966535995e45 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2eb7f1be88190964ddcbb6a05f021 |
completed | Feb. 28, 2026, 1:19 p.m. |
| PD | Predicate disambiguation | batch_69a2e9571bd88190b6fcb16f21604720 |
completed | Feb. 28, 2026, 1:10 p.m. |
| PDg | Predicate description generation | batch_69a2ea0a4c448190a8a179daa9b90645 |
completed | Feb. 28, 2026, 1:13 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.