Triple
T14240617
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ian Kahn |
E352992
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Ian Kahn |
E352992
|
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: Ian Kahn | Statement: [Ian Kahn, name, Ian Kahn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ian Kahn Context triple: [Ian Kahn, name, Ian Kahn]
-
A.
Ian Kahn
chosen
Ian Kahn is an American actor best known for playing George Washington on the television series "Turn: Washington's Spies."
-
B.
John Kahn
John Kahn was an American bassist and producer best known for his long-running collaboration with Jerry Garcia across multiple projects.
-
C.
Paul Kahn
Paul Kahn is a notable individual whose specific public recognition or field of prominence is not clearly identifiable from the given information.
-
D.
David Kahne
David Kahne is an American record producer and engineer known for his work with major artists across rock and pop, including Paul McCartney.
-
E.
Neil Kagan
Neil Kagan is an American editor and author known for producing richly illustrated historical and reference books, particularly for National Geographic.
- 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_69d8278adc7c8190a9218d69bce3c4e6 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de62432fb48190b153805b85c4f2d2 |
completed | April 14, 2026, 3:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fde15f2cd8819085f949fa5122af2a |
completed | May 8, 2026, 1:13 p.m. |
Created at: April 10, 2026, 1:08 a.m.