Triple
T21029513
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hod |
E518027
|
entity |
| Predicate | archangel |
P60554
|
FINISHED |
| Object | Michael |
—
|
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: Michael | Statement: [Hod, archangel, Michael]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Context triple: [Hod, archangel, Michael]
-
A.
Michael
Michael is a central figure in the crime drama "Sleepers," whose traumatic experiences and quest for justice drive much of the film’s emotional and moral conflict.
-
B.
Michael
Michael is the conflicted protagonist of the 2006 romantic dramedy "The Last Kiss," whose struggle with commitment and impending fatherhood drives the film’s central emotional tension.
-
C.
Michael
chosen
Michael is a common masculine given name of Hebrew origin meaning "Who is like God?"
-
D.
Michael
"Michael" is a 1996 fantasy-comedy film starring John Travolta as an unconventional archangel visiting Earth.
-
E.
Michael
Michael is a central fictional character in the Australian television drama series "The Newsreader," which explores the personal and professional lives of journalists in the 1980s.
- 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_69e0b503275c8190afd9a163f997c709 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fc7fd3ec81908ac237047f9b0d7a |
completed | April 21, 2026, 4:26 a.m. |
Created at: April 16, 2026, 1:55 p.m.