Triple
T18425308
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hannon |
E442122
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Hannan |
—
|
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: Hannan | Statement: [Hannon, hasVariant, Hannan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hannan Context triple: [Hannon, hasVariant, Hannan]
-
A.
Hannan
chosen
Hannan is a coastal city in southern Osaka Prefecture, Japan, known for its fishing industry and proximity to Osaka Bay.
-
B.
Harlen
Harlen is the given name of J Harlen Bretz, the American geologist known for his pioneering work on the Missoula Floods and the Channeled Scablands.
-
C.
Garson
Garson is a masculine given name most notably associated with American writer and director Garson Kanin.
-
D.
Nordhoff
Nordhoff is a surname most notably associated with American author and journalist Charles Nordhoff.
-
E.
Tilghman
Tilghman is a surname most notably associated with Shirley M. Tilghman, a prominent molecular biologist and former president of Princeton University.
- 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_69d8b9eb8a508190a942fd75ebd8b1dc |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e51b12606081908ea320fd8d5554c6 |
completed | April 19, 2026, 6:12 p.m. |
Created at: April 10, 2026, 10:47 a.m.