Triple
T17973404
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bill Willingham |
E449402
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Fables |
—
|
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: Fables | Statement: [Bill Willingham, notableWork, Fables]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fables Context triple: [Bill Willingham, notableWork, Fables]
-
A.
Fables
Fables is a collection of satirical verse tales by John Gay that use animal characters and moral lessons to comment on human nature and society.
-
B.
Fables
Fables is a collection of medieval verse tales by Marie de France that adapt and moralize traditional animal stories and folktales.
-
C.
Fables
chosen
Fables is a comic book series created by Bill Willingham that reimagines classic fairy-tale and folklore characters living in exile in modern-day New York City.
-
D.
Aesop's fables
Aesop's fables are a classic collection of short moral stories, traditionally attributed to the ancient Greek storyteller Aesop, that use animals and everyday situations to illustrate ethical lessons.
-
E.
FablesBookI
FablesBookI is the first book in Jean de La Fontaine’s celebrated collection of verse fables, introducing many of his most famous moral tales.
- 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_69d8b9f9927c8190a006110c8b996e61 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e4b1fca04481908f0dd875953fd82f |
completed | April 19, 2026, 10:44 a.m. |
Created at: April 10, 2026, 10:22 a.m.