Triple
T37153697
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Lark |
E920432
|
entity |
| Predicate | EnglishAdaptor |
P120583
|
FINISHED |
| Object | Lillian Hellman |
—
|
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: Lillian Hellman | Statement: [The Lark, EnglishAdaptor, Lillian Hellman]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: EnglishAdaptor Context triple: [The Lark, EnglishAdaptor, Lillian Hellman]
-
A.
EnglishTranslation
Indicates that one expression is the English-language translation equivalent of another expression.
-
B.
EnglishTranslatorOfWork
chosen
Indicates that one entity is the person who translated a specific work into English.
-
C.
EnglishObjective
Indicates that an entity has English language proficiency or achievement as a goal or target to be attained.
-
D.
EnglishCommander
Indicates that one entity serves as a military commander or leader for English forces in relation to another entity.
-
E.
englishPart
Indicates that one entity is a component, segment, or subset of another within the context of English (e.g., English text, language, or content).
- 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_69f76e9f87c08190b4c8f7fafbd8345a |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fb344c60f8819090f2e21e1e61d621 |
completed | May 6, 2026, 12:30 p.m. |
| PD | Predicate disambiguation | batch_69fb2f642db08190b562725502c74ea6 |
completed | May 6, 2026, 12:09 p.m. |
Created at: May 3, 2026, 4:15 p.m.