Triple
T33255128
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Immortal Wife |
E851354
|
entity |
| Predicate | hasInfluentialCharacter |
P36788
|
FINISHED |
| Object | Jessie Benton Frémont |
—
|
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: Jessie Benton Frémont | Statement: [Immortal Wife, hasInfluentialCharacter, Jessie Benton Frémont]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasInfluentialCharacter Context triple: [Immortal Wife, hasInfluentialCharacter, Jessie Benton Frémont]
-
A.
hasMainCharacterFrom
Indicates that a work of fiction has a main character who originates from or belongs to a specified place, group, or source.
-
B.
hasFictionalLeadCharacter
Indicates that a creative work features a particular fictional character as its main or leading protagonist.
-
C.
hasEnigmaticCharacter
Indicates that something possesses a mysterious, puzzling, or difficult-to-interpret quality or nature.
-
D.
influencesCharacter
chosen
Indicates that one entity affects, shapes, or alters the traits, behavior, or development of another entity’s character.
-
E.
hasIconicCharacter
Indicates that something is associated with a character widely recognized as emblematic or highly representative of it.
- 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_69f34963135c819084e7f1d483421f00 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69ffdf47d9608190830ca23d9cef6409 |
completed | May 10, 2026, 1:28 a.m. |
| PD | Predicate disambiguation | batch_69ffdf00e2b4819082dd5cb78f316baf |
completed | May 10, 2026, 1:27 a.m. |
Created at: May 1, 2026, 1:31 a.m.