Triple
T21269120
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Why I Wake Early |
E524207
|
entity |
| Predicate | hasPoem |
P21160
|
FINISHED |
| Object | The Moth |
—
|
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: The Moth | Statement: [Why I Wake Early, hasPoem, The Moth]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: The Moth Context triple: [Why I Wake Early, hasPoem, The Moth]
-
A.
The Moth
chosen
The Moth is a nonprofit organization dedicated to the art and craft of live, unscripted storytelling, best known for its storytelling events and popular podcast.
-
B.
The Moth Effect
The Moth Effect is an Australian sketch comedy series known for its surreal, satirical take on contemporary issues, directed by filmmaker Gracie Otto.
-
C.
Thirteen Conversations About One Thing
Thirteen Conversations About One Thing is a 2001 independent drama film that interweaves multiple New Yorkers’ lives to explore themes of chance, happiness, and moral responsibility.
-
D.
The New Yorker Radio Hour
The New Yorker Radio Hour is a weekly audio program that features in-depth reporting, interviews, and storytelling inspired by the journalism and culture of The New Yorker magazine.
-
E.
Show and Tell
Show and Tell is a neural network-based image captioning model developed by Google that automatically generates natural language descriptions for images.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b5156d7881909bd4f83676590715 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e73651115081908b5083ba818a6bb1 |
completed | April 21, 2026, 8:33 a.m. |
Created at: April 16, 2026, 4:01 p.m.