Triple
T18177680
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Falling Man (article) |
E435204
|
entity |
| Predicate | workBy |
P12692
|
FINISHED |
| Object | Tom Junod |
—
|
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: Tom Junod | Statement: [The Falling Man (article), workBy, Tom Junod]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tom Junod Context triple: [The Falling Man (article), workBy, Tom Junod]
-
A.
Tom Junod
chosen
Tom Junod is an American journalist and Esquire writer known for his deeply reported, emotionally resonant profiles and feature stories.
-
B.
Gilles Martin
Gilles Martin is a French entrepreneur best known as the founder and long-time leader of Eurofins Scientific, a global laboratory testing and analytical services company.
-
C.
Roger Wehrli
Roger Wehrli is a Hall of Fame American football cornerback best known for his standout career with the St. Louis Cardinals in the NFL during the 1970s.
-
D.
Roger Seibel
Roger Seibel is an American audio mastering engineer known for his work on numerous indie and alternative rock records.
-
E.
James Lesure
James Lesure is an American television actor known for his roles in series such as Las Vegas, For Your Love, and Good Girls.
- 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_69d8b90c7ec081909b4694ccecb449c6 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4df5a72008190bd2e56205b995a87 |
completed | April 19, 2026, 1:57 p.m. |
Created at: April 10, 2026, 10:31 a.m.