Triple
T21897868
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hannibal |
E540726
|
entity |
| Predicate | characterPortrayed |
P1507
|
FINISHED |
| Object | Will Graham |
—
|
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: Will Graham | Statement: [Hannibal, characterPortrayed, Will Graham]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Will Graham Context triple: [Hannibal, characterPortrayed, Will Graham]
-
A.
Will Graham
chosen
Will Graham is a gifted but psychologically fragile FBI profiler known for his uncanny ability to empathize with and understand the minds of serial killers in Thomas Harris's Hannibal Lecter series.
-
B.
John Graham
John Graham is an Australian Labor Party politician who serves as a senior minister in the New South Wales government under Premier Chris Minns.
-
C.
John Graham
John Graham was a British colonial military officer after whom the South African town of Grahamstown (now Makhanda) was named.
-
D.
Doug Graham
Doug Graham is a New Zealand politician and lawyer who served as a senior National Party cabinet minister, notably overseeing major legal and Treaty of Waitangi settlement reforms.
-
E.
Ian Graham
Ian Graham was a prominent British Mayanist archaeologist and epigrapher known for his extensive documentation and study of ancient Maya sites and inscriptions.
- 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_69e0c47b4e8c81908c8076eaa4c8e4f2 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f11fc7c70c81908b9bb4de68616e41 |
completed | April 28, 2026, 8:59 p.m. |
Created at: April 16, 2026, 7:07 p.m.