Triple
T1100584
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ASEAN Secretariat |
E24369
|
entity |
| Predicate | numberOfMemberStatesServed |
P1590
|
FINISHED |
| Object | 10 |
—
|
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: 10 | Statement: [ASEAN Secretariat, numberOfMemberStatesServed, 10]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfMemberStatesServed Context triple: [ASEAN Secretariat, numberOfMemberStatesServed, 10]
-
A.
numberOfMemberStates
chosen
Indicates the total count of member states associated with a given entity or organization.
-
B.
hasMembersPerState
Indicates a relationship that specifies how many members are associated with each state.
-
C.
numberOfMemberStateMissions
Indicates the total count of missions associated with or undertaken by the member states in a given context.
-
D.
numberOfStates
Indicates the total count of distinct states or conditions associated with an entity or system.
-
E.
numberOfStatesRepresented
Indicates how many distinct states are represented or covered in a given context or entity.
- 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_69a4940542308190ac2a0b1f730b7cfc |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4b9c079f48190a0e0ddda182f7a01 |
completed | March 1, 2026, 10:12 p.m. |
| PD | Predicate disambiguation | batch_69a4b745ef3481909a7ce4647c8567b3 |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:43 p.m.