Triple
T31538554
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gabrielle Darley |
E804676
|
entity |
| Predicate | sourceOfCharacterName |
P148817
|
FINISHED |
| Object | “Gabrielle” in The Red Kimona |
—
|
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: “Gabrielle” in The Red Kimona | Statement: [Gabrielle Darley, sourceOfCharacterName, “Gabrielle” in The Red Kimona]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sourceOfCharacterName Context triple: [Gabrielle Darley, sourceOfCharacterName, “Gabrielle” in The Red Kimona]
-
A.
characterNameOrigin
chosen
Indicates the source or inspiration from which a character’s name is derived.
-
B.
originOfCharacter
Indicates the source or place from which a character originates or is created.
-
C.
characterOrigin
Indicates the source, background, or initial context from which a character originates.
-
D.
primarySourcesForCharacter
Indicates that certain sources are the main or original references documenting or describing a given character.
-
E.
characterName
Indicates that an entity has a specific name used to identify its 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_69f348d03ef88190a2b73d7b94b9e02d |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6fb19063c81909466b329655c8583 |
completed | May 3, 2026, 7:36 a.m. |
| PD | Predicate disambiguation | batch_69f6f969b4cc8190afb473a2d8b110bc |
completed | May 3, 2026, 7:29 a.m. |
Created at: April 30, 2026, 10:05 p.m.