Triple
T6455600
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uproar |
E141985
|
entity |
| Predicate | writer |
P1360
|
FINISHED |
| Object |
Tyrone Kelsie
Tyrone Kelsie is an author best known for writing the work titled "Uproar."
|
E597546
|
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: Tyrone Kelsie | Statement: [Uproar, writer, Tyrone Kelsie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tyrone Kelsie Context triple: [Uproar, writer, Tyrone Kelsie]
-
A.
Darryl Hickman
Darryl Hickman is an American former child actor and film and television performer known for roles in classic Hollywood films and later work as a television executive and acting coach.
-
B.
Tarik Matthews
Tarik Matthews is the child of Gloria Matthews.
-
C.
Tully Marshall
Tully Marshall was an American character actor of the silent and early sound film era, known for his prolific work in supporting roles across numerous Hollywood productions.
-
D.
Darrell Porter
Darrell Porter was an American Major League Baseball catcher best known for his standout postseason performances in the late 1970s and early 1980s, including key roles with the Kansas City Royals and St. Louis Cardinals.
-
E.
Kevin Stoney
Kevin Stoney was a British character actor best known for his villainous roles in classic science fiction television, particularly in series like Doctor Who.
- 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: Tyrone Kelsie Triple: [Uproar, writer, Tyrone Kelsie]
Generated description
Tyrone Kelsie is an author best known for writing the work titled "Uproar."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tyrone Kelsie Target entity description: Tyrone Kelsie is an author best known for writing the work titled "Uproar."
-
A.
Darryl Hickman
Darryl Hickman is an American former child actor and film and television performer known for roles in classic Hollywood films and later work as a television executive and acting coach.
-
B.
Tarik Matthews
Tarik Matthews is the child of Gloria Matthews.
-
C.
Tully Marshall
Tully Marshall was an American character actor of the silent and early sound film era, known for his prolific work in supporting roles across numerous Hollywood productions.
-
D.
Darrell Porter
Darrell Porter was an American Major League Baseball catcher best known for his standout postseason performances in the late 1970s and early 1980s, including key roles with the Kansas City Royals and St. Louis Cardinals.
-
E.
Kevin Stoney
Kevin Stoney was a British character actor best known for his villainous roles in classic science fiction television, particularly in series like Doctor Who.
- 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_69c008d2f91c8190a8178767a35e08fc |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c069d4d588819090e8a56c46c0bfe9 |
completed | March 22, 2026, 10:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c65fce95008190b821aafdddbe8296 |
completed | March 27, 2026, 10:45 a.m. |
| NEDg | Description generation | batch_69c660e0c6b48190bba7162153af6d80 |
completed | March 27, 2026, 10:50 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c6613a3d1881908a93ffdf3e98ee98 |
completed | March 27, 2026, 10:51 a.m. |
Created at: March 22, 2026, 4:48 p.m.