Triple
T10588784
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Thick of It |
E249926
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object | Malcolm Tucker |
E834527
|
NE FINISHED |
How this triple was built (2 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: Malcolm Tucker | Statement: [The Thick of It, mainCharacter, Malcolm Tucker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Malcolm Tucker Context triple: [The Thick of It, mainCharacter, Malcolm Tucker]
-
A.
Malcolm Tucker
chosen
Malcolm Tucker is a foul-mouthed, ruthlessly manipulative political spin doctor from the British television series "The Thick of It."
-
B.
Phil O'Donnell
Phil O'Donnell was a Scottish professional footballer and club captain best known for his influential midfield career at Motherwell and Celtic before his tragic on-field death in 2007.
-
C.
Mitch Martin
Mitch Martin is the hapless, newly single protagonist of the comedy film "Old School," whose midlife crisis leads him to start a wild fraternity with his friends.
-
D.
Chris Mills
Chris Mills is a former American professional basketball player who played as a forward in the NBA during the 1990s and early 2000s.
-
E.
Ben Marino
Ben Marino is a character in the musical "Fiorello!" who serves as a politically savvy associate within the world of New York City machine politics.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d381c9d3d48190a29ee491e1696a0e |
completed | April 6, 2026, 9:50 a.m. |
| NER | Named-entity recognition | batch_69d527793c588190bfe3a5261eb7f919 |
completed | April 7, 2026, 3:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d94b9440548190bff01847a940266b |
completed | April 10, 2026, 7:12 p.m. |
Created at: April 6, 2026, 12:40 p.m.