Triple
T10563986
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DG NEAR |
E249300
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object | DG NEAR |
E249300
|
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: DG NEAR | Statement: [DG NEAR, shortName, DG NEAR]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DG NEAR Context triple: [DG NEAR, shortName, DG NEAR]
-
A.
DG NEAR
chosen
DG NEAR is the European Commission department responsible for managing the EU’s neighbourhood policy and overseeing enlargement negotiations with candidate and potential candidate countries.
-
B.
NEAR Shoemaker
NEAR Shoemaker was a NASA robotic space probe that studied the near-Earth asteroid Eros, becoming the first spacecraft to orbit and land on an asteroid.
-
C.
DAO
DAO (Data Access Objects) is an older Microsoft data access technology that provides an object-oriented interface for working with databases, particularly Jet/Access databases.
-
D.
NEM
NEM refers to Hungary’s New Economic Mechanism, a major 1968 reform program that introduced market-oriented elements into the country’s socialist planned economy.
-
E.
Nexo
Nexo is a town on the eastern coast of the Danish island of Bornholm, known for its fishing harbor and coastal scenery.
- 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_69d381c8bd708190acf3d275c908251e |
completed | April 6, 2026, 9:50 a.m. |
| NER | Named-entity recognition | batch_69d527224b808190b996ae970393f9c3 |
completed | April 7, 2026, 3:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d9349bb7748190b5afb492e78d1128 |
completed | April 10, 2026, 5:34 p.m. |
Created at: April 6, 2026, 12:36 p.m.