Triple
T12072951
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dead Man’s Ransom |
E287469
|
entity |
| Predicate | literarySettingContext |
P45019
|
FINISHED |
| Object | medieval England |
—
|
LITERAL 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: medieval England | Statement: [Dead Man’s Ransom, literarySettingContext, medieval England]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: literarySettingContext Context triple: [Dead Man’s Ransom, literarySettingContext, medieval England]
-
A.
hasLiterarySetting
Indicates that a literary work is set in, or primarily takes place within, a particular location or environment.
-
B.
narrativeSettingOfWork
chosen
Indicates that a particular place, time, or context serves as the narrative setting in which a work’s story or events occur.
-
C.
hasLiteraryContext
Indicates that something is associated with, situated within, or explained by a particular literary context (such as a work, genre, period, or interpretive framework).
-
D.
placeOfSetting
Indicates the location or environment where an event, scene, or situation takes place.
-
E.
literaryScript
Indicates a relationship where an entity serves as the written text or script of a literary work, such as a play, film, or other narrative production.
- F. None of above.
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_69d6ab4846e081908ee7bbd66a6d3459 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9100b4ca8819084845ca4c13e34ce |
completed | April 10, 2026, 2:58 p.m. |
| PD | Predicate disambiguation | batch_69d902bda47c8190b94860b31df4a98c |
completed | April 10, 2026, 2:01 p.m. |
Created at: April 8, 2026, 9:48 p.m.