Triple
T21996893
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | INTOSAI |
E543228
|
entity |
| Predicate | hasRegionalOrganization |
P2785
|
FINISHED |
| Object | EUROSAI |
—
|
NE NERFINISHED |
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: EUROSAI | Statement: [INTOSAI, hasRegionalOrganization, EUROSAI]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: EUROSAI Context triple: [INTOSAI, hasRegionalOrganization, EUROSAI]
-
A.
EUROSAI
chosen
EUROSAI is the regional organization of European supreme audit institutions that promotes cooperation, knowledge sharing, and capacity building in public-sector auditing across Europe.
-
B.
EURASHE
EURASHE is a European association representing and supporting professional higher education institutions, such as universities of applied sciences and similar practice-oriented providers.
-
C.
CSIEA
CSIEA is a U.S. federal law that regulates the import and export of controlled substances to prevent drug trafficking and abuse.
-
D.
ESSAIM
ESSAIM is a French military signals intelligence microsatellite constellation designed for electronic surveillance and space-based reconnaissance.
-
E.
SIAEC
SIAEC is a Singapore-based aircraft maintenance, repair, and overhaul (MRO) company that provides engineering and technical services to airlines worldwide.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e2c814c8190837d072789000486 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f12765fb0c81908f7b7acda065ee2f |
completed | April 28, 2026, 9:32 p.m. |
Created at: April 16, 2026, 8:19 p.m.