Triple
T3321713
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Mirror Crack'd |
E69808
|
entity |
| Predicate | filmingLocation |
P40
|
FINISHED |
| Object | Kent |
E5977
|
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: Kent | Statement: [The Mirror Crack'd, filmingLocation, Kent]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kent Context triple: [The Mirror Crack'd, filmingLocation, Kent]
-
A.
Kent
Kent is a suburban city in King County, Washington, known as a residential and industrial hub within the greater Seattle metropolitan area.
-
B.
Kent
chosen
Kent is a county in southeastern England known for its historic towns, coastal landscapes, and nickname "the Garden of England."
-
C.
Kent
Kent is a brand of filtered cigarettes historically marketed as a "safer" smoking option and produced by the Lorillard Tobacco Company.
-
D.
Kent, England
Kent, England is a historic county in southeastern England known for its coastal towns, rich agricultural land, and nickname "the Garden of England."
-
E.
Kersey
Kersey is a historic village in Suffolk, England, noted for its picturesque medieval buildings and traditional rural character.
- 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_69ad85a1829881908942c14075644d0d |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb13d13a88190828d9a03fd0865ce |
completed | March 8, 2026, 5:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b31a7954248190b0c7b5d6ab3c6687 |
completed | March 12, 2026, 7:56 p.m. |
Created at: March 8, 2026, 3:11 p.m.