Triple
T15974075
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Lion of Athens |
E387398
|
entity |
| Predicate | appliedTo |
P1129
|
FINISHED |
| Object | Edwin Flack |
E83344
|
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: Edwin Flack | Statement: [The Lion of Athens, appliedTo, Edwin Flack]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Edwin Flack Context triple: [The Lion of Athens, appliedTo, Edwin Flack]
-
A.
Edwin Flack
chosen
Edwin Flack was an Australian middle-distance runner and accountant who became his country's first Olympic champion by winning gold medals at the inaugural modern Games in 1896.
-
B.
Samuel Bayliss
Samuel Bayliss was a notable local figure significant enough to have Bayliss Park named in his honor, likely for his contributions to the surrounding community or its development.
-
C.
Homer Pennock
Homer Pennock was an early settler and entrepreneur after whom the Alaskan city of Homer is named.
-
D.
Pete Lattimer
Pete Lattimer is a Secret Service agent and artifact hunter who serves as one of the central protagonists in the science-fiction television series "Warehouse 13."
-
E.
John Bosworth
John Bosworth is a seasoned, old-school Texas businessman and executive who serves as a mentor and stabilizing force amid the volatile tech innovators in the television drama "Halt and Catch Fire."
- 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_69d86da94ccc819083d187f5dc6a123e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1572b667c8190b28d0556e45422bb |
completed | April 16, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffeb84979c8190b9f8d5a78b1cc880 |
completed | May 10, 2026, 2:20 a.m. |
Created at: April 10, 2026, 4:54 a.m.