Triple
T17902757
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael C. Hall |
E447623
|
entity |
| Predicate | playedCharacter |
P1507
|
FINISHED |
| Object | Dexter Morgan |
—
|
NE NERFINISHED |
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: Dexter Morgan | Statement: [Michael C. Hall, playedCharacter, Dexter Morgan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dexter Morgan Context triple: [Michael C. Hall, playedCharacter, Dexter Morgan]
-
A.
Dexter Morgan
chosen
Dexter Morgan is the fictional forensic blood-spatter analyst and vigilante serial killer who serves as the antihero protagonist of the television series "Dexter."
-
B.
Sinister Dexter
Sinister Dexter is a long-running 2000 AD comic strip about two wisecracking hitmen, Finnigan Sinister and Ramone Dexter, operating in a violent, futuristic European city.
-
C.
Norman Colin Dexter
Norman Colin Dexter was an English crime writer best known for creating the Inspector Morse detective novels.
-
D.
Harlan Dexter
Harlan Dexter is a wealthy, morally corrupt former actor turned powerful businessman who serves as a central antagonist in the darkly comedic neo-noir film "Kiss Kiss Bang Bang."
-
E.
Michael Ripps
Michael Ripps is a film editor known for his work on the movie "Stakeout."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9f59bd48190a6fc925a855b8bac |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e49e99c3188190aead24cd3d48c7a7 |
completed | April 19, 2026, 9:21 a.m. |
Created at: April 10, 2026, 10:19 a.m.