Triple
T17383851
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Falk |
E422635
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Greta Falk
Greta Falk is a relatively obscure individual whose specific public achievements or biographical details are not widely documented.
|
E1266480
|
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: Greta Falk | Statement: [Falk, hasNotableBearer, Greta Falk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Greta Falk Context triple: [Falk, hasNotableBearer, Greta Falk]
-
A.
Greta Lindström
Greta Lindström is a Swedish actress known for her roles in early 20th-century Scandinavian cinema.
-
B.
Greta Granstedt
Greta Granstedt was an American film and television actress active primarily in the 1930s and 1940s, known for her supporting roles in crime dramas and B-movies.
-
C.
Greta Lovisa Gustafsson
Greta Lovisa Gustafsson, better known as Greta Garbo, was a legendary Swedish-American film actress renowned for her enigmatic screen presence and iconic roles during Hollywood’s silent and early sound eras.
-
D.
Greta Lundgren
Greta Lundgren is a daughter of Swedish actor and martial artist Dolph Lundgren.
-
E.
Kristina Lugn
Kristina Lugn was a Swedish poet, playwright, and member of the Swedish Academy known for her darkly humorous and psychologically incisive works.
- 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: Greta Falk Triple: [Falk, hasNotableBearer, Greta Falk]
Generated description
Greta Falk is a relatively obscure individual whose specific public achievements or biographical details are not widely documented.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Greta Falk Target entity description: Greta Falk is a relatively obscure individual whose specific public achievements or biographical details are not widely documented.
-
A.
Greta Lindström
Greta Lindström is a Swedish actress known for her roles in early 20th-century Scandinavian cinema.
-
B.
Greta Granstedt
Greta Granstedt was an American film and television actress active primarily in the 1930s and 1940s, known for her supporting roles in crime dramas and B-movies.
-
C.
Greta Lovisa Gustafsson
Greta Lovisa Gustafsson, better known as Greta Garbo, was a legendary Swedish-American film actress renowned for her enigmatic screen presence and iconic roles during Hollywood’s silent and early sound eras.
-
D.
Greta Lundgren
Greta Lundgren is a daughter of Swedish actor and martial artist Dolph Lundgren.
-
E.
Kristina Lugn
Kristina Lugn was a Swedish poet, playwright, and member of the Swedish Academy known for her darkly humorous and psychologically incisive works.
- 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_69d889d6535c81908be333c01deaec4e |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a87d9948190986021f1fb5a4e00 |
completed | April 19, 2026, 2:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a019ff72ca08190a5ea8a1ba6330728 |
completed | May 11, 2026, 9:23 a.m. |
| NEDg | Description generation | batch_6a01a1887d408190baf6d68b17917c09 |
completed | May 11, 2026, 9:29 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a01a2777c188190960046cb7c0b8404 |
completed | May 11, 2026, 9:33 a.m. |
Created at: April 10, 2026, 5:45 a.m.