Triple
T21719407
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tonopah silver boom |
E536114
|
entity |
| Predicate | associatedPerson |
P2308
|
FINISHED |
| Object | Jim Butler |
—
|
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: Jim Butler | Statement: [Tonopah silver boom, associatedPerson, Jim Butler]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jim Butler Context triple: [Tonopah silver boom, associatedPerson, Jim Butler]
-
A.
Jim Butler
chosen
Jim Butler was an American prospector credited with sparking Nevada’s Tonopah silver boom in the early 1900s.
-
B.
Mark Schweiker
Mark Schweiker is an American politician who served as the 44th governor of Pennsylvania in the early 2000s.
-
C.
Jonathan Mardukas
Jonathan Mardukas is a neurotic yet principled accountant-turned-informant on the run from both the mob and law enforcement in the action-comedy film "Midnight Run."
-
D.
Roger Marshall
Roger Marshall is an American Republican politician and physician who has served as a U.S. Representative from Kansas and later as a U.S. Senator.
-
E.
Scott Butler
Scott Butler is known primarily as a member of the prominent Butler family of Indianapolis, descended from Ovid Butler, the abolitionist lawyer and founder of Butler University.
- 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_69e0c46c6dd88190a595375fa6ebd701 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69efd96de818819084c268d4775a8e3a |
completed | April 27, 2026, 9:47 p.m. |
Created at: April 16, 2026, 6:47 p.m.