Triple
T14661092
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Theodore Twombly |
E344237
|
entity |
| Predicate | closeFriend |
P8712
|
FINISHED |
| Object |
Paul
Paul is a character in the film "Her," known as Theodore Twombly’s supportive and easygoing close friend.
|
E1112391
|
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: Paul | Statement: [Theodore Twombly, closeFriend, Paul]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paul Context triple: [Theodore Twombly, closeFriend, Paul]
-
A.
Paul
Paul is the middle-aged American widower portrayed by Marlon Brando in the controversial 1972 film "Last Tango in Paris."
-
B.
Paul
Paul is a laid-back, charming sperm donor whose unexpected involvement with his biological children disrupts a lesbian couple’s family dynamic in the film "The Kids Are All Right."
-
C.
Paul
Paul is a 2011 sci-fi comedy film about two British geeks who encounter a wisecracking alien during a road trip across the United States.
-
D.
Paul
Paul is a fictional character from Wallace Thurman's Harlem Renaissance novel "Infants of the Spring," which satirically portrays life in a bohemian Black artists' colony.
-
E.
Paul
Paul is a central fictional character in the Australian television drama series "The Newsreader," which explores the lives and challenges of 1980s newsroom staff.
- 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: Paul Triple: [Theodore Twombly, closeFriend, Paul]
Generated description
Paul is a character in the film "Her," known as Theodore Twombly’s supportive and easygoing close friend.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Paul Target entity description: Paul is a character in the film "Her," known as Theodore Twombly’s supportive and easygoing close friend.
-
A.
Paul
Paul is a character from the film "Nobody’s Business," contributing to the story’s exploration of personal and family relationships.
-
B.
Paul
Paul is a central character in the psychological horror film "It Comes at Night," portrayed as a protective family man struggling to safeguard his loved ones amid a mysterious, apocalyptic threat.
-
C.
Paul
Paul is a character in the crime drama film "Never Die Alone," which follows the violent, intertwined lives of drug dealers and those around them.
-
D.
Paul
Paul is a laid-back, charming sperm donor whose unexpected involvement with his biological children disrupts a lesbian couple’s family dynamic in the film "The Kids Are All Right."
-
E.
Paul
Paul is a character from the "Wild" universe, known for his role within its adventurous, nature-centered narrative.
- 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_69d822e283fc8190a0e4c235cf880052 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb51c84448190a1f5fa9ab8a1e2c4 |
completed | April 14, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdd5e28f848190a3d686bc11336601 |
completed | May 8, 2026, 12:24 p.m. |
| NEDg | Description generation | batch_69fdd65a6c24819088fb18ffcdfe6404 |
completed | May 8, 2026, 12:26 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fdd6f172288190ba7097518b1a971e |
completed | May 8, 2026, 12:28 p.m. |
Created at: April 10, 2026, 1:27 a.m.