Triple
T6330194
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ASEAN Defence Ministers’ Meeting |
E141957
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object | ADMM |
E585595
|
NE 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: ADMM | Statement: [ASEAN Defence Ministers’ Meeting, abbreviation, ADMM]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ADMM Context triple: [ASEAN Defence Ministers’ Meeting, abbreviation, ADMM]
-
A.
ADMM
chosen
ADMM is a regional forum that brings together the defence ministers of ASEAN member states to discuss and coordinate security and defence cooperation in Southeast Asia.
-
B.
AdaDelta
AdaDelta is an adaptive learning rate optimization algorithm for training neural networks that improves upon methods like RMSProp by eliminating the need to manually set a global learning rate.
-
C.
Adam: A Method for Stochastic Optimization
"Adam: A Method for Stochastic Optimization" is a highly influential machine learning paper that introduces the Adam optimizer, a widely used adaptive gradient-based optimization algorithm for training deep neural networks.
-
D.
Automatic Adam
Automatic Adam is the nickname of Adam Vinatieri, a legendary NFL placekicker renowned for his clutch, game-winning field goals in high-pressure situations.
-
E.
AdaGrad
AdaGrad is an adaptive gradient descent optimization algorithm that adjusts learning rates for individual parameters based on their historical gradients, often improving convergence in sparse settings.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69c008d201748190917e69c41ba3f978 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0651334c08190a9514faa36e7812d |
completed | March 22, 2026, 9:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c62d3a68f881908de1d9e70e00cb02 |
completed | March 27, 2026, 7:09 a.m. |
Created at: March 22, 2026, 4:30 p.m.