Triple
T124731
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tegel |
E2522
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Berlin (state) |
E5567
|
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: Berlin (state) | Statement: [Tegel, locatedIn, Berlin (state)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Berlin (state) Context triple: [Tegel, locatedIn, Berlin (state)]
-
A.
Lower Saxony
Lower Saxony is a large federal state in northwestern Germany known for its diverse landscapes, strong industrial base, and historic cities such as Hanover and Göttingen.
-
B.
Mecklenburg-Vorpommern
Mecklenburg-Vorpommern is a federal state in northeastern Germany known for its Baltic Sea coastline, numerous lakes, and relatively low population density.
-
C.
Berlin
chosen
Berlin is the capital and largest city of Germany, historically significant as a focal point of Cold War tensions and a major cultural, political, and economic center in Europe.
-
D.
Baden-Württemberg
Baden-Württemberg is a federal state in southwest Germany known for its strong economy, automotive industry, and cities like Stuttgart, Heidelberg, and Freiburg.
-
E.
North Rhine-Westphalia
North Rhine-Westphalia is Germany’s most populous federal state, known for its major industrial regions, cultural hubs like Cologne and Düsseldorf, and numerous universities and research institutions.
- 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_69a251b54ea88190b18281669f59b4c0 |
completed | Feb. 28, 2026, 2:23 a.m. |
| NER | Named-entity recognition | batch_69a2573e4e6481908252dfef2e34f46e |
completed | Feb. 28, 2026, 2:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a399a12344819097028715c166ecb7 |
completed | March 1, 2026, 1:42 a.m. |
Created at: Feb. 28, 2026, 2:27 a.m.