Triple
T12301636
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mayerling (1936 film) |
E293238
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object |
Jean Worms
Jean Worms was a French film and stage actor active in the early 20th century.
|
E974024
|
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: Jean Worms | Statement: [Mayerling (1936 film), castMember, Jean Worms]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jean Worms Context triple: [Mayerling (1936 film), castMember, Jean Worms]
-
A.
Heidelberg
Heidelberg is a suburb of Melbourne, Australia, known for its historic role in Australian Impressionism and its location along the Yarra River.
-
B.
Heidelberg
Heidelberg is a South African town known for its historical significance and role as a regional service and commercial center.
-
C.
Heidelberg
Heidelberg is a historic university city in southwestern Germany renowned for its picturesque old town, castle ruins, and one of Europe’s oldest universities.
-
D.
Landsberg
Landsberg is a town in the Saalekreis district of the German state of Saxony-Anhalt.
-
E.
Kaiserslautern
Kaiserslautern is a city in southwestern Germany known for its historic old town, technical university, and prominent football club 1. FC Kaiserslautern.
- 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: Jean Worms Triple: [Mayerling (1936 film), castMember, Jean Worms]
Generated description
Jean Worms was a French film and stage actor active in the early 20th century.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jean Worms Target entity description: Jean Worms was a French film and stage actor active in the early 20th century.
-
A.
Heidelberg
Heidelberg is a historic university city in southwestern Germany renowned for its picturesque old town, castle ruins, and one of Europe’s oldest universities.
-
B.
Heidelberg
Heidelberg is a suburb of Melbourne, Australia, known for its historic role in Australian Impressionism and its location along the Yarra River.
-
C.
Heidelberg
Heidelberg is a South African town known for its historical significance and role as a regional service and commercial center.
-
D.
Landsberg
Landsberg is a town in the Saalekreis district of the German state of Saxony-Anhalt.
-
E.
Kaiserslautern
Kaiserslautern is a city in southwestern Germany known for its historic old town, technical university, and prominent football club 1. FC Kaiserslautern.
- 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_69d6ab6a2b50819082f6aedd32ed608a |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93edb59908190bcef9d0cdc11081f |
completed | April 10, 2026, 6:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f61e7d757881908ac6af2b70a6dafe |
completed | May 2, 2026, 3:55 p.m. |
| NEDg | Description generation | batch_69f61f5cc5608190a67a888eb5136ada |
completed | May 2, 2026, 3:59 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f62006afcc8190b8e3b55a5fd8eaca |
completed | May 2, 2026, 4:02 p.m. |
Created at: April 8, 2026, 9:53 p.m.