Triple
T36042275
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charlotte Bartlett |
E1042572
|
entity |
| Predicate | visitsLocationInFiction |
P185284
|
FINISHED |
| Object | Florence |
—
|
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: Florence | Statement: [Charlotte Bartlett, visitsLocationInFiction, Florence]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: visitsLocationInFiction Context triple: [Charlotte Bartlett, visitsLocationInFiction, Florence]
-
A.
hasBranchInFictionalLocation
Indicates that an organization maintains a branch, office, or presence within a fictional or imaginary location.
-
B.
locatedNearFiction
Indicates that one fictional entity or place is situated close to another within an imagined or narrative context.
-
C.
voyageDestinationInFiction
Indicates that a fictional voyage or journey is directed toward or ends at a particular destination within a work of fiction.
-
D.
locationWithinFiction
Indicates that one fictional location is situated inside or contained within another fictional location.
-
E.
placeOfFictionalEvent
Indicates the location where a fictional event is depicted as occurring within a narrative or story.
- F. None of above. chosen
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_69f76e2d7e8c8190bac4e90734566799 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7bbf906d8819099020e548dd56bc9 |
completed | May 3, 2026, 9:19 p.m. |
| PD | Predicate disambiguation | batch_69f7b9a2dcf88190a7c9e109e41267be |
completed | May 3, 2026, 9:09 p.m. |
| PDg | Predicate description generation | batch_69f7bbf812cc8190a16917c5daaff2df |
completed | May 3, 2026, 9:19 p.m. |
Created at: May 3, 2026, 4:07 p.m.