Triple
T4114645
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Armin Mueller-Stahl |
E90261
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Lola
Lola is a 1981 West German drama film directed by Rainer Werner Fassbinder, in which Armin Mueller-Stahl plays a prominent role in a story set in postwar Germany.
|
E412873
|
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: Lola | Statement: [Armin Mueller-Stahl, notableWork, Lola]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lola Context triple: [Armin Mueller-Stahl, notableWork, Lola]
-
A.
Lola
Lola is a fictional character portrayed by British actor Chiwetel Ejiofor.
-
B.
Carla
Carla is a feminine given name commonly used in various languages, often considered the female form of Carl or Charles.
-
C.
Lila
Lila is a central female character in Max Frisch’s novel "Mein Name sei Gantenbein," around whom the narrator constructs one of his imagined lives and relationships.
-
D.
Lela
Lela is a feminine given name used in various cultures, often as a variant of Leila or Layla.
-
E.
Lola Lane
Lola Lane was an American film actress best known as one of the Lane Sisters, who appeared in numerous Hollywood productions during the 1930s and 1940s.
- 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: Lola Triple: [Armin Mueller-Stahl, notableWork, Lola]
Generated description
Lola is a 1981 West German drama film directed by Rainer Werner Fassbinder, in which Armin Mueller-Stahl plays a prominent role in a story set in postwar Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lola Target entity description: Lola is a 1981 West German drama film directed by Rainer Werner Fassbinder, in which Armin Mueller-Stahl plays a prominent role in a story set in postwar Germany.
-
A.
Lola
Lola is a fictional character portrayed by British actor Chiwetel Ejiofor.
-
B.
Carla
Carla is a feminine given name commonly used in various languages, often considered the female form of Carl or Charles.
-
C.
Lila
Lila is a central female character in Max Frisch’s novel "Mein Name sei Gantenbein," around whom the narrator constructs one of his imagined lives and relationships.
-
D.
Lela
Lela is a feminine given name used in various cultures, often as a variant of Leila or Layla.
-
E.
Lola Lane
Lola Lane was an American film actress best known as one of the Lane Sisters, who appeared in numerous Hollywood productions during the 1930s and 1940s.
- 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_69aed95c080881908125e30c5dcdc6f8 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af01f07c688190a6e3689667587247 |
completed | March 9, 2026, 5:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b56b8e30448190a1ac0df969c83631 |
completed | March 14, 2026, 2:07 p.m. |
| NEDg | Description generation | batch_69b56c18d7a48190b00f06b3987253fe |
completed | March 14, 2026, 2:09 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b56ca787f08190b59ed9113658ba91 |
completed | March 14, 2026, 2:11 p.m. |
Created at: March 9, 2026, 3:41 p.m.