Triple
T17095074
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Holden Snyder |
E414825
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Snyder |
E61047
|
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: Snyder | Statement: [Holden Snyder, familyName, Snyder]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Snyder Context triple: [Holden Snyder, familyName, Snyder]
-
A.
Snyder
chosen
Snyder is a surname most prominently associated with Dan Snyder, the American businessman and former owner of the NFL’s Washington Commanders.
-
B.
Sneider
Sneider is a family surname most notably associated with American novelist Vern Sneider.
-
C.
Nolan
Nolan is a common Irish surname that has been borne by numerous notable figures across fields such as film, sports, and politics.
-
D.
Zack Snyder
Zack Snyder is an American filmmaker known for his visually stylized, action-driven comic book and superhero adaptations such as 300, Watchmen, and multiple DC Extended Universe films.
-
E.
Korman
Korman is a surname most famously associated with American comedic actor Harvey Korman, known for his work on The Carol Burnett Show and in Mel Brooks films.
- 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_69d886cfc8e88190b05ba466edd35591 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dbfc9158819081689d3d594a1908 |
completed | April 18, 2026, 7:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a012eedfd7c8190b267dedd403f5f2b |
completed | May 11, 2026, 1:20 a.m. |
Created at: April 10, 2026, 5:35 a.m.