Triple
T1715025
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | It (2017 film) |
E37270
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object | Mike Hanlon |
E226369
|
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: Mike Hanlon | Statement: [It (2017 film), character, Mike Hanlon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mike Hanlon Context triple: [It (2017 film), character, Mike Hanlon]
-
A.
Mike Hanlon
chosen
Mike Hanlon is a central member of the Losers' Club in Stephen King's horror novel "It," known for his role as the group's historian and the only one to remain in Derry into adulthood.
-
B.
Kevin Hageman
Kevin Hageman is an American screenwriter and producer known for his work on animated and family films and television series, including contributions to The Lego Movie franchise.
-
C.
Jeff Henley
Jeff Henley is an American business executive best known for his long tenure as Oracle Corporation’s chief financial officer and later chairman of the board.
-
D.
Mike Konopacki
Mike Konopacki is an American political cartoonist known for his labor- and social-justice-focused comics and graphic works.
-
E.
Michael Rogers
Michael Rogers is a relatively common personal name shared by multiple individuals across fields such as politics, sports, and the arts, rather than referring to one singular widely recognized figure.
- 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_69a8861912dc8190931af43b4b9158a7 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa633349248190822e560fde817fc7 |
completed | March 6, 2026, 5:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae26e9748081909532426be2f198d1 |
completed | March 9, 2026, 1:48 a.m. |
Created at: March 4, 2026, 7:30 p.m.