Triple
T40800
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Angela Merkel |
E805
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Angela Merkel |
E805
|
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: Angela Merkel | Statement: [Angela Merkel, name, Angela Merkel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Angela Merkel Context triple: [Angela Merkel, name, Angela Merkel]
-
A.
Angela Merkel
chosen
Angela Merkel is a German politician who served as Chancellor of Germany from 2005 to 2021 and became one of the most influential leaders in Europe and the world.
-
B.
António Guterres
António Guterres is a Portuguese politician and diplomat who has served as the ninth Secretary-General of the United Nations and was formerly Prime Minister of Portugal and UN High Commissioner for Refugees.
-
C.
Alexander De Croo
Alexander De Croo is a Belgian liberal politician who has served as Prime Minister of Belgium, leading the federal government.
-
D.
Trygve Lie
Trygve Lie was a Norwegian politician and diplomat who became the first Secretary-General of the United Nations, serving from 1946 to 1952.
-
E.
Theodor
Theodor "Ted" Nelson is an American pioneer of information technology best known for coining the term "hypertext" and envisioning global hyperlinked document systems.
- 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_69a247a8f6c08190bac804906d62ed5a |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a24ae0b7c4819092220568e8e52ad5 |
completed | Feb. 28, 2026, 1:54 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a24e6222408190bc317b90aea16849 |
completed | Feb. 28, 2026, 2:09 a.m. |
Created at: Feb. 28, 2026, 1:46 a.m.