Triple
T8494197
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | City |
E201052
|
entity |
| Predicate | containsStory |
P6847
|
FINISHED |
| Object | "Aesop" |
E314182
|
NE FINISHED |
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: "Aesop" | Statement: [City, containsStory, "Aesop"]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: "Aesop" Context triple: [City, containsStory, "Aesop"]
-
A.
Aesopus
Aesopus is a figure from Greek mythology, known as a river god associated with one of the many rivers personified in ancient Greek religion.
-
B.
Aesop's fables
chosen
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.
-
C.
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.
-
D.
Fables
Fables is a collection of medieval verse tales by Marie de France that adapt and moralize traditional animal stories and folktales.
-
E.
Fables
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.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ca831ee390819095fae73400bbfafc |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe57c01f881908cb77c8c834ac08d |
completed | March 31, 2026, 3:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce3a5c260c8190bc7012a04363d260 |
completed | April 2, 2026, 9:43 a.m. |
Created at: March 30, 2026, 6:13 p.m.