Triple
T7843482
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jeremy Brock |
E181860
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
The Widowmaker
The Widowmaker is a 1990 British drama film about a junior doctor confronting systemic failures in the National Health Service, written by Jeremy Brock.
|
E698982
|
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: The Widowmaker | Statement: [Jeremy Brock, notableWork, The Widowmaker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: The Widowmaker Context triple: [Jeremy Brock, notableWork, The Widowmaker]
-
A.
Crimson Widow
Crimson Widow is an alias used by Yelena Belova, a highly trained Russian spy and assassin who has operated as both an adversary and occasional ally of Black Widow in Marvel Comics.
-
B.
Assassin
Assassin is a rapper known for his energetic delivery and collaborations within the hip-hop and dancehall scenes.
-
C.
Assassin
Assassin is a stand-up comedy special by Margaret Cho known for its sharp political satire and candid social commentary.
-
D.
Lady Death
Lady Death is the wartime nickname of Lyudmila Pavlichenko, a famed Soviet World War II sniper credited with hundreds of confirmed kills.
-
E.
Daizy
Daizy is an alternative spelling or variant form of the given name Daisy, often used as a personal name.
- 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: The Widowmaker Triple: [Jeremy Brock, notableWork, The Widowmaker]
Generated description
The Widowmaker is a 1990 British drama film about a junior doctor confronting systemic failures in the National Health Service, written by Jeremy Brock.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: The Widowmaker Target entity description: The Widowmaker is a 1990 British drama film about a junior doctor confronting systemic failures in the National Health Service, written by Jeremy Brock.
-
A.
Crimson Widow
Crimson Widow is an alias used by Yelena Belova, a highly trained Russian spy and assassin who has operated as both an adversary and occasional ally of Black Widow in Marvel Comics.
-
B.
Assassin
Assassin is a rapper known for his energetic delivery and collaborations within the hip-hop and dancehall scenes.
-
C.
Assassin
Assassin is a stand-up comedy special by Margaret Cho known for its sharp political satire and candid social commentary.
-
D.
Lady Death
Lady Death is the wartime nickname of Lyudmila Pavlichenko, a famed Soviet World War II sniper credited with hundreds of confirmed kills.
-
E.
Daizy
Daizy is an alternative spelling or variant form of the given name Daisy, often used as a personal name.
- 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_69ca8285d6488190a95d4c02d7354b53 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb163c72248190b53bc53980e8ac0f |
completed | March 31, 2026, 12:33 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb5aded4048190b18604963784352c |
completed | March 31, 2026, 5:25 a.m. |
| NEDg | Description generation | batch_69cb762dd8348190bf74be4e7f5df1e7 |
completed | March 31, 2026, 7:22 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cbb24068908190977b266366e5ceea |
completed | March 31, 2026, 11:38 a.m. |
Created at: March 30, 2026, 4:48 p.m.