Triple
T16828609
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Broederbond |
E409086
|
entity |
| Predicate | notableMember |
P10
|
FINISHED |
| Object |
Nico Diederichs
Nico Diederichs was a South African National Party politician who served as the country’s State President from 1975 to 1978.
|
E1235404
|
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: Nico Diederichs | Statement: [Broederbond, notableMember, Nico Diederichs]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nico Diederichs Context triple: [Broederbond, notableMember, Nico Diederichs]
-
A.
Nico Hillenbrand
Nico Hillenbrand is a German professional footballer known for playing as a defender in the lower tiers of German football.
-
B.
Kai Wiesinger
Kai Wiesinger is a German actor known for his roles in film and television, often appearing in historical dramas and popular German cinema.
-
C.
Oliver Korittke
Oliver Korittke is a German actor known for his roles in film and television, particularly in comedies and crime series.
-
D.
Oliver Borchert
Oliver Borchert is a German local politician who serves as the mayor of the municipality of Wandlitz in Brandenburg.
-
E.
Nico Habermann
Nico Habermann was a German-American computer scientist known for his contributions to programming languages, operating systems, and software engineering, and for his influential academic leadership at Carnegie Mellon University.
- 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: Nico Diederichs Triple: [Broederbond, notableMember, Nico Diederichs]
Generated description
Nico Diederichs was a South African National Party politician who served as the country’s State President from 1975 to 1978.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nico Diederichs Target entity description: Nico Diederichs was a South African National Party politician who served as the country’s State President from 1975 to 1978.
-
A.
Nico Hillenbrand
Nico Hillenbrand is a German professional footballer known for playing as a defender in the lower tiers of German football.
-
B.
Kai Wiesinger
Kai Wiesinger is a German actor known for his roles in film and television, often appearing in historical dramas and popular German cinema.
-
C.
Oliver Korittke
Oliver Korittke is a German actor known for his roles in film and television, particularly in comedies and crime series.
-
D.
Oliver Borchert
Oliver Borchert is a German local politician who serves as the mayor of the municipality of Wandlitz in Brandenburg.
-
E.
Nico Habermann
Nico Habermann was a German-American computer scientist known for his contributions to programming languages, operating systems, and software engineering, and for his influential academic leadership at Carnegie Mellon University.
- 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_69d88394566c8190b3dcbdc72935f7fa |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b3151350819097b1c375e6df8986 |
completed | April 18, 2026, 4:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00b2a0ac148190a7a7edebcb67c040 |
completed | May 10, 2026, 4:30 p.m. |
| NEDg | Description generation | batch_6a00b35ea8f88190ae33e8a2f906d133 |
completed | May 10, 2026, 4:33 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00b3d14b3c819081f435777f47eca3 |
completed | May 10, 2026, 4:35 p.m. |
Created at: April 10, 2026, 5:23 a.m.