Triple
T13186082
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hager |
E313855
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Werner Hager
Werner Hager is an individual notable enough to be recognized as a prominent bearer of the surname Hager.
|
E1026001
|
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: Werner Hager | Statement: [Hager, hasNotableBearer, Werner Hager]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Werner Hager Context triple: [Hager, hasNotableBearer, Werner Hager]
-
A.
Gerhard Feige
Gerhard Feige is a German Roman Catholic prelate and theologian who serves as the bishop of Magdeburg.
-
B.
Dieter W. Heermann
Dieter W. Heermann is a physicist known for his work in computational physics and biophysics, particularly in the modeling of complex systems such as polymers and chromatin.
-
C.
Hans-Peter Kriegel
Hans-Peter Kriegel is a German computer scientist renowned for his influential contributions to data mining and database systems, particularly in clustering and similarity search.
-
D.
Harald Ganzinger
Harald Ganzinger was a prominent German computer scientist known for his influential work in automated theorem proving and term rewriting systems.
-
E.
Juergen Weigert
Juergen Weigert is a software developer best known for his significant contributions to the GNU Screen terminal multiplexer project.
- 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: Werner Hager Triple: [Hager, hasNotableBearer, Werner Hager]
Generated description
Werner Hager is an individual notable enough to be recognized as a prominent bearer of the surname Hager.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Werner Hager Target entity description: Werner Hager is an individual notable enough to be recognized as a prominent bearer of the surname Hager.
-
A.
Gerhard Feige
Gerhard Feige is a German Roman Catholic prelate and theologian who serves as the bishop of Magdeburg.
-
B.
Dieter W. Heermann
Dieter W. Heermann is a physicist known for his work in computational physics and biophysics, particularly in the modeling of complex systems such as polymers and chromatin.
-
C.
Hans-Peter Kriegel
Hans-Peter Kriegel is a German computer scientist renowned for his influential contributions to data mining and database systems, particularly in clustering and similarity search.
-
D.
Harald Ganzinger
Harald Ganzinger was a prominent German computer scientist known for his influential work in automated theorem proving and term rewriting systems.
-
E.
Juergen Weigert
Juergen Weigert is a software developer best known for his significant contributions to the GNU Screen terminal multiplexer project.
- 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_69d806ae1e08819090d95bfe1538cc17 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98c4b663c8190b0b18f0785f7b57d |
completed | April 10, 2026, 11:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6f5f7a304819081c4f51631948cbd |
completed | May 3, 2026, 7:15 a.m. |
| NEDg | Description generation | batch_69f6f694f8c48190adce4cddbf63777f |
completed | May 3, 2026, 7:17 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6f7d8bfa0819097b3d9175bc56933 |
completed | May 3, 2026, 7:23 a.m. |
Created at: April 9, 2026, 9:15 p.m.