Triple
T2029184
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Klaus Mann |
E44476
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Anja und Esther
Anja und Esther is a 1925 play by German writer Klaus Mann that explores themes of youth, love, and identity in a bohemian milieu.
|
E226879
|
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: Anja und Esther | Statement: [Klaus Mann, notableWork, Anja und Esther]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anja und Esther Context triple: [Klaus Mann, notableWork, Anja und Esther]
-
A.
Eva
Eva is a feminine given name of Hebrew origin, equivalent to "Eve" and widely used in many languages and cultures.
-
B.
Christa
Christa was the first name of Christa McAuliffe, the American teacher and astronaut selected as the first private citizen to fly in space.
-
C.
Bettina
Bettina is a feminine given name of Hebrew origin, often considered a diminutive of Elisabeth or Benedetta and used in various European languages.
-
D.
Veronika
Veronika is the troubled young protagonist of Paulo Coelho's novel "Veronika Decides to Die," whose suicide attempt leads her to a transformative stay in a mental institution.
-
E.
Milena
Milena is the birth name of actress Mila Kunis, a Ukrainian-born American performer known for roles in "That '70s Show" and "Black Swan."
- 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: Anja und Esther Triple: [Klaus Mann, notableWork, Anja und Esther]
Generated description
Anja und Esther is a 1925 play by German writer Klaus Mann that explores themes of youth, love, and identity in a bohemian milieu.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Anja und Esther Target entity description: Anja und Esther is a 1925 play by German writer Klaus Mann that explores themes of youth, love, and identity in a bohemian milieu.
-
A.
Eva
Eva is a feminine given name of Hebrew origin, equivalent to "Eve" and widely used in many languages and cultures.
-
B.
Christa
Christa was the first name of Christa McAuliffe, the American teacher and astronaut selected as the first private citizen to fly in space.
-
C.
Bettina
Bettina is a feminine given name of Hebrew origin, often considered a diminutive of Elisabeth or Benedetta and used in various European languages.
-
D.
Veronika
Veronika is the troubled young protagonist of Paulo Coelho's novel "Veronika Decides to Die," whose suicide attempt leads her to a transformative stay in a mental institution.
-
E.
Milena
Milena is the birth name of actress Mila Kunis, a Ukrainian-born American performer known for roles in "That '70s Show" and "Black Swan."
- 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_69a889144f2481909932f0746a93023d |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb9136f888190b0fd03530e9eda1e |
completed | March 7, 2026, 5:35 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae0afea7848190b53b8813a567879d |
completed | March 8, 2026, 11:49 p.m. |
| NEDg | Description generation | batch_69ae0b8cda248190b88b353c1768c3d7 |
completed | March 8, 2026, 11:51 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae0c31f00c8190bb29098f95ee4cb9 |
completed | March 8, 2026, 11:54 p.m. |
Created at: March 4, 2026, 7:38 p.m.