Triple
T14972643
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tokyo Daigaku |
E373360
|
entity |
| Predicate | memberOf |
P10
|
FINISHED |
| Object | ASEA-UNINET |
E45219
|
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: ASEA-UNINET | Statement: [Tokyo Daigaku, memberOf, ASEA-UNINET]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ASEA-UNINET Context triple: [Tokyo Daigaku, memberOf, ASEA-UNINET]
-
A.
ASEA-UNINET
chosen
ASEA-UNINET is an international academic network that promotes cooperation in higher education and research between universities in Europe and Southeast Asia.
-
B.
ASEA
ASEA was a major Swedish electrical engineering and power company that became a global leader in industrial technology before merging to form ABB.
-
C.
ASEA
ASEA is the acronym for the African Securities Exchanges Association, a regional body that promotes the development and integration of stock exchanges across Africa.
-
D.
ASEC
ASEC is the commonly used abbreviation for the ASEAN Secretariat, the administrative body that supports and coordinates the activities of the Association of Southeast Asian Nations.
-
E.
ENAS
ENAS (Efficient Neural Architecture Search) is a method that dramatically reduces the computational cost of neural architecture search by sharing parameters among many candidate architectures within a single super-network.
- 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_69d85ccbbcd48190acb56e7cf104d8ad |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded6e767608190940eb6f16ea97451 |
completed | April 15, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe8be8af688190832efb00695f8b20 |
completed | May 9, 2026, 1:20 a.m. |
Created at: April 10, 2026, 2:50 a.m.