Triple
T20661693
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cell |
E507773
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Tom McCourt
Tom McCourt is a pragmatic, resourceful middle-aged man who becomes one of the key survivors and companions in Stephen King’s post-apocalyptic horror novel "Cell."
|
E1444399
|
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: Tom McCourt | Statement: [Cell, mainCharacter, Tom McCourt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tom McCourt Context triple: [Cell, mainCharacter, Tom McCourt]
-
A.
Neil McCarthy
Neil McCarthy was a British character actor known for his distinctive appearance and roles in film and television, including fantasy and historical epics.
-
B.
Mike O'Shea
Mike O'Shea is a cinematographer known for his work on the film "61*," contributing to its visual style and storytelling.
-
C.
John McMullen
John McMullen was a 19th-century Catholic bishop and civic leader known for his role in establishing educational and religious institutions in the American Midwest.
-
D.
Kevin McCann
Kevin McCann is a voice actor who contributed to the animated film "Puss in Boots: The Last Wish."
-
E.
Sean McDonough
Sean McDonough is an American sportscaster best known for his long career calling Major League Baseball and college sports on national television.
- 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: Tom McCourt Triple: [Cell, mainCharacter, Tom McCourt]
Generated description
Tom McCourt is a pragmatic, resourceful middle-aged man who becomes one of the key survivors and companions in Stephen King’s post-apocalyptic horror novel "Cell."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tom McCourt Target entity description: Tom McCourt is a pragmatic, resourceful middle-aged man who becomes one of the key survivors and companions in Stephen King’s post-apocalyptic horror novel "Cell."
-
A.
Neil McCarthy
Neil McCarthy was a British character actor known for his distinctive appearance and roles in film and television, including fantasy and historical epics.
-
B.
Mike O'Shea
Mike O'Shea is a cinematographer known for his work on the film "61*," contributing to its visual style and storytelling.
-
C.
John McMullen
John McMullen was a 19th-century Catholic bishop and civic leader known for his role in establishing educational and religious institutions in the American Midwest.
-
D.
Kevin McCann
Kevin McCann is a voice actor who contributed to the animated film "Puss in Boots: The Last Wish."
-
E.
Sean McDonough
Sean McDonough is an American sportscaster best known for his long career calling Major League Baseball and college sports on national television.
- 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_69e0b4c059bc81908ea762cd73ea4424 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6b2f2ee4081908df9ba897c9dfc98 |
completed | April 20, 2026, 11:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a08cd5afe5081909e20b796dd9f0908 |
completed | May 16, 2026, 8:02 p.m. |
| NEDg | Description generation | batch_6a08d175206c8190b119bb1a2d06462f |
completed | May 16, 2026, 8:20 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a08d293523c8190aaa01c6c73c9afd5 |
completed | May 16, 2026, 8:24 p.m. |
Created at: April 16, 2026, 11:44 a.m.