Triple
T6363518
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Federal Council (Germany) |
E143169
|
entity |
| Predicate | represents |
P129
|
FINISHED |
| Object |
Länder
Länder are the individual federal states that make up the Federal Republic of Germany, each with its own government and significant legislative powers.
|
E588135
|
NE FINISHED |
How this triple was built (4 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: Länder | Statement: [Federal Council (Germany), represents, Länder]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Länder Context triple: [Federal Council (Germany), represents, Länder]
-
A.
Negara
Negara is a town in western Bali, Indonesia, known as an administrative and commercial center in the Jembrana Regency.
-
B.
Nationen
Nationen is a Norwegian newspaper known for its focus on rural issues, agriculture, and district politics.
-
C.
Parland
Parland is a surname most notably associated with Alfred Parland, a Russian architect of British descent known for designing the Church of the Savior on Spilled Blood in Saint Petersburg.
-
D.
Lage Landen
Lage Landen is the historical Low Countries region in Western Europe, roughly encompassing present-day Belgium, the Netherlands, and Luxembourg.
-
E.
Landes
Landes is a department in southwestern France known for its vast Atlantic coastline, extensive pine forests, and popular surfing beaches.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Länder Triple: [Federal Council (Germany), represents, Länder]
Generated description
Länder are the individual federal states that make up the Federal Republic of Germany, each with its own government and significant legislative powers.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Länder Target entity description: Länder are the individual federal states that make up the Federal Republic of Germany, each with its own government and significant legislative powers.
-
A.
Negara
Negara is a town in western Bali, Indonesia, known as an administrative and commercial center in the Jembrana Regency.
-
B.
Nationen
Nationen is a Norwegian newspaper known for its focus on rural issues, agriculture, and district politics.
-
C.
Parland
Parland is a surname most notably associated with Alfred Parland, a Russian architect of British descent known for designing the Church of the Savior on Spilled Blood in Saint Petersburg.
-
D.
Lage Landen
Lage Landen is the historical Low Countries region in Western Europe, roughly encompassing present-day Belgium, the Netherlands, and Luxembourg.
-
E.
Landes
Landes is a department in southwestern France known for its vast Atlantic coastline, extensive pine forests, and popular surfing beaches.
- F. None of above. chosen
Provenance (5 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_69c008d7a9c4819098d647ec47776917 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0680d51a4819098a6bcd3dfd73be4 |
completed | March 22, 2026, 10:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c62d73a6ac8190a02602c3506e4226 |
completed | March 27, 2026, 7:10 a.m. |
| NEDg | Description generation | batch_69c62f2abb7481909a8d6b6a3b07db37 |
completed | March 27, 2026, 7:18 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c62fcd18a0819089fa5f5912f432aa |
completed | March 27, 2026, 7:20 a.m. |
Created at: March 22, 2026, 4:32 p.m.