Triple
T9321973
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dudley Manlove |
E224287
|
entity |
| Predicate | hasFamilyName |
P18
|
FINISHED |
| Object |
Manlove
Manlove is an English-language surname borne by various notable individuals, including figures in politics, sports, and the arts.
|
E791657
|
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: Manlove | Statement: [Dudley Manlove, hasFamilyName, Manlove]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Manlove Context triple: [Dudley Manlove, hasFamilyName, Manlove]
-
A.
The MatchMaker
The MatchMaker is a romantic comedy film best known for its lighthearted story about love and relationships, released in the late 1990s.
-
B.
The Love Match
The Love Match is a British stage comedy (later adapted for film and television) best known for featuring actress Thora Hird in a prominent role.
-
C.
Lovehunter
Lovehunter is a 1979 hard rock album by British band Whitesnake, noted for its bluesy sound and controversial cover art.
-
D.
Matchmakers
Matchmakers is a popular Ukrainian comedy television series produced by Kvartal 95 Studio that follows the humorous clashes and relationships between two very different families.
-
E.
Mr. Right
Mr. Right is a 2015 action-romantic comedy film in which Sam Rockwell plays a eccentric hitman who falls in love while being pursued by his former employers.
- 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: Manlove Triple: [Dudley Manlove, hasFamilyName, Manlove]
Generated description
Manlove is an English-language surname borne by various notable individuals, including figures in politics, sports, and the arts.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Manlove Target entity description: Manlove is an English-language surname borne by various notable individuals, including figures in politics, sports, and the arts.
-
A.
The MatchMaker
The MatchMaker is a romantic comedy film best known for its lighthearted story about love and relationships, released in the late 1990s.
-
B.
The Love Match
The Love Match is a British stage comedy (later adapted for film and television) best known for featuring actress Thora Hird in a prominent role.
-
C.
Lovehunter
Lovehunter is a 1979 hard rock album by British band Whitesnake, noted for its bluesy sound and controversial cover art.
-
D.
Matchmakers
Matchmakers is a popular Ukrainian comedy television series produced by Kvartal 95 Studio that follows the humorous clashes and relationships between two very different families.
-
E.
Mr. Right
Mr. Right is a 2015 action-romantic comedy film in which Sam Rockwell plays a eccentric hitman who falls in love while being pursued by his former employers.
- 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_69ca8426d48481909596360f7791c7dd |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd36f2bd288190bb1556a88d9e90f3 |
completed | April 1, 2026, 3:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0c7d70b1c8190a254f58efc370624 |
completed | April 4, 2026, 8:12 a.m. |
| NEDg | Description generation | batch_69d0cbb518708190a896b0bf1fb7c9c7 |
completed | April 4, 2026, 8:28 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d0cc49215c8190894fe206d0230134 |
completed | April 4, 2026, 8:31 a.m. |
Created at: March 30, 2026, 7:38 p.m.