Triple
T22507993
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Langen Foundation |
E556438
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object |
Marianne Langen
Marianne Langen was a German art collector and philanthropist whose collection and patronage led to the establishment of the Langen Foundation museum.
|
E1540000
|
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: Marianne Langen | Statement: [Langen Foundation, namedAfter, Marianne Langen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marianne Langen Context triple: [Langen Foundation, namedAfter, Marianne Langen]
-
A.
Johanna Lange
Johanna Lange was the wife of German philosopher and neo-Kantian thinker Friedrich Albert Lange.
-
B.
Marianne Tromlitz
Marianne Tromlitz was the mother of the renowned Romantic-era pianist and composer Clara Schumann.
-
C.
Marianne Stenshagen
Marianne Stenshagen is a Norwegian speed skater who has competed at the international level representing Norway.
-
D.
Marianne Willisch
Marianne Willisch is an artist and designer associated with the New Bauhaus movement in Chicago.
-
E.
Marianne Sägebrecht
Marianne Sägebrecht is a German actress known for her distinctive character roles in films such as "Sugarbaby" and "Bagdad Café."
- 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: Marianne Langen Triple: [Langen Foundation, namedAfter, Marianne Langen]
Generated description
Marianne Langen was a German art collector and philanthropist whose collection and patronage led to the establishment of the Langen Foundation museum.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Marianne Langen Target entity description: Marianne Langen was a German art collector and philanthropist whose collection and patronage led to the establishment of the Langen Foundation museum.
-
A.
Johanna Lange
Johanna Lange was the wife of German philosopher and neo-Kantian thinker Friedrich Albert Lange.
-
B.
Marianne Tromlitz
Marianne Tromlitz was the mother of the renowned Romantic-era pianist and composer Clara Schumann.
-
C.
Marianne Stenshagen
Marianne Stenshagen is a Norwegian speed skater who has competed at the international level representing Norway.
-
D.
Marianne Willisch
Marianne Willisch is an artist and designer associated with the New Bauhaus movement in Chicago.
-
E.
Marianne Sägebrecht
Marianne Sägebrecht is a German actress known for her distinctive character roles in films such as "Sugarbaby" and "Bagdad Café."
- 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_69e11e555edc81909ca803587dafd747 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15d5dec7c8190bf71ef76a2dfe9a4 |
completed | April 29, 2026, 1:22 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0b17eb056c81908f1617c01c490d1b |
completed | May 18, 2026, 1:45 p.m. |
| NEDg | Description generation | batch_6a0b187392188190912568c024737542 |
completed | May 18, 2026, 1:47 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0b1901e4d481909f831b88714bbe24 |
completed | May 18, 2026, 1:49 p.m. |
Created at: April 16, 2026, 8:50 p.m.