Triple
T35381164
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr. Crewe |
E1022651
|
entity |
| Predicate | hasCountryOfFictionalSetting |
P44462
|
FINISHED |
| Object | England |
E1791
|
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: England | Statement: [Mr. Crewe, hasCountryOfFictionalSetting, England]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCountryOfFictionalSetting Context triple: [Mr. Crewe, hasCountryOfFictionalSetting, England]
-
A.
nationalityOfFictionalSetting
Indicates that a fictional setting is associated with, or belongs to, a particular nationality or country.
-
B.
locatedInFictionalCountry
Indicates that an entity exists or is situated within a country that is fictional rather than real.
-
C.
hasFictionalLocation
Indicates that an entity is associated with, set in, or takes place within a location that exists only in fiction rather than in the real world.
-
D.
countryOfFictionalContext
chosen
Indicates that a work of fiction is primarily set in, or contextually associated with, a particular country.
-
E.
basedInFictionalSetting
Indicates that an entity’s primary location or setting exists within a fictional or imaginary world rather than the real world.
- F. None of above.
Provenance (4 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_69f76df28d8c819089f2c5799fe7d079 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a037c8c34f88190ace26f555827f23e |
completed | May 12, 2026, 7:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a3852d4c56081908bf75207ebb6d6b7 |
completed | June 21, 2026, 9:08 p.m. |
| PD | Predicate disambiguation | batch_6a037a0324d08190ac5b610cc0f6a38c |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:03 p.m.