Triple
T33878373
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ellen Nussey |
E868417
|
entity |
| Predicate | providedLettersTo |
P182765
|
FINISHED |
| Object | Elizabeth Gaskell |
—
|
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: Elizabeth Gaskell | Statement: [Ellen Nussey, providedLettersTo, Elizabeth Gaskell]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: providedLettersTo Context triple: [Ellen Nussey, providedLettersTo, Elizabeth Gaskell]
-
A.
presentedLettersTo
chosen
Indicates that one entity formally gave or showed letters to another entity.
-
B.
hasLettersFor
Indicates that one entity possesses or contains written correspondence intended for another entity.
-
C.
usesLettersFrom
Indicates that one entity is formed or constructed using the letters that appear in another entity.
-
D.
usesAdditionalLettersFrom
Indicates that one entity forms or derives its representation by incorporating extra letters taken from another entity beyond those originally present.
-
E.
settingOfManyLetters
Indicates a setting or context in which many letters (such as written messages or characters) occur, are exchanged, or are central to the situation.
- 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_69f34995b81c8190acdb45cea5a10eff |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69fe189fec148190aeef51b417ba15b0 |
completed | May 8, 2026, 5:08 p.m. |
| PD | Predicate disambiguation | batch_69fe17285b0881908de7569d8dbd20bd |
completed | May 8, 2026, 5:02 p.m. |
Created at: May 1, 2026, 1:48 a.m.