Triple
T9937113
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr. & Mrs. Smith (2005 film) |
E193985
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
John Smith
John Smith is the male lead, a suburban husband secretly working as a professional assassin, in the 2005 action-comedy film "Mr. & Mrs. Smith."
|
E832839
|
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: John Smith | Statement: [Mr. & Mrs. Smith (2005 film), mainCharacter, John Smith]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: John Smith Context triple: [Mr. & Mrs. Smith (2005 film), mainCharacter, John Smith]
-
A.
John
John is the husband of Martha Rainsborough.
-
B.
John
John is the given name of John Vlissides, a software engineer best known as one of the “Gang of Four” authors of the influential book *Design Patterns: Elements of Reusable Object-Oriented Software*.
-
C.
John
John is the given name of Sir John Tusa, a prominent British arts administrator and former managing director of the BBC World Service and the Barbican Centre.
-
D.
John
John is the given name of Prince Alexander John of Wales, a short-lived British royal infant of the 19th century.
-
E.
John
John is the given name of John Stewart, Earl of Mar, a Scottish nobleman and political figure.
- 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: John Smith Triple: [Mr. & Mrs. Smith (2005 film), mainCharacter, John Smith]
Generated description
John Smith is the male lead, a suburban husband secretly working as a professional assassin, in the 2005 action-comedy film "Mr. & Mrs. Smith."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: John Smith Target entity description: John Smith is the male lead, a suburban husband secretly working as a professional assassin, in the 2005 action-comedy film "Mr. & Mrs. Smith."
-
A.
John Smith
John Smith was an English soldier, explorer, and leader who played a pivotal role in the establishment and survival of the Jamestown colony in early colonial Virginia.
-
B.
John Smith
John Smith is a high-ranking Nazi official in the alternate-history television series "The Man in the High Castle," whose loyalty, ambition, and moral conflicts drive much of the show's central drama.
-
C.
John Smith
John Smith was a 19th-century British officer credited with bringing the Ajanta Caves to modern attention after rediscovering them in 1819.
-
D.
John Smith
John Smith was one of the African American defendants tried alongside Dr. Ossian Sweet in the landmark 1925 Detroit case involving racial violence and self-defense.
-
E.
John Smith
John Smith was a delegate from South Carolina who served in the Continental Congress during the American Revolutionary era.
- 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_69ca82e409348190a393777356b80a2a |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb5e4e19881909879b394090d6629 |
completed | April 2, 2026, 12:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d23d4528108190b38111bb36832a67 |
completed | April 5, 2026, 10:45 a.m. |
| NEDg | Description generation | batch_69d23eb1c1f481908404225dcccd0697 |
completed | April 5, 2026, 10:51 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d242aea6a08190a73a836e59865c35 |
completed | April 5, 2026, 11:08 a.m. |
Created at: March 30, 2026, 8:44 p.m.