Triple
T21447567
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frenchman’s Bend |
E529115
|
entity |
| Predicate | fictionalRelation |
P106091
|
FINISHED |
| Object | nearby fictional town of Jefferson |
—
|
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: nearby fictional town of Jefferson | Statement: [Frenchman’s Bend, fictionalRelation, nearby fictional town of Jefferson]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalRelation Context triple: [Frenchman’s Bend, fictionalRelation, nearby fictional town of Jefferson]
-
A.
fictionalRelationship
Indicates a relationship that exists only within a fictional or imagined context between entities.
-
B.
relatedToInFiction
chosen
Indicates that one entity is connected to another within a fictional context, such as a story, universe, or narrative work.
-
C.
literaryRelationship
Indicates a relationship between entities that are connected through literature, such as authorship, influence, adaptation, or other text-based associations.
-
D.
characterActorRelationship
Indicates a relationship where an actor portrays or is associated with a specific character in a work.
-
E.
relationshipToCharacter
Indicates the specific type of personal, social, or narrative connection that one entity has to a given character.
- 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_69e0c457579481909db68053ed99750c |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9e9d04548819086594c20faa5217d |
completed | April 23, 2026, 9:43 a.m. |
| PD | Predicate disambiguation | batch_69e631df1b38819088d3604854e697b4 |
completed | April 20, 2026, 2:02 p.m. |
Created at: April 16, 2026, 6:06 p.m.