Triple
T20656885
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | William J. Usery Jr. |
E507649
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Usery |
—
|
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: Usery | Statement: [William J. Usery Jr., familyName, Usery]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Usery Context triple: [William J. Usery Jr., familyName, Usery]
-
A.
Usery
chosen
Usery is a surname most notably associated with William J. Usery Jr., a prominent American labor union activist and U.S. Secretary of Labor.
-
B.
Userin
Userin is a small settlement in the Mecklenburgische Seenplatte region of Mecklenburg-Vorpommern in northeastern Germany.
-
C.
Meuser
Meuser is a German-language surname borne by various individuals, including American politician Dan Meuser.
-
D.
Userkare
Userkare was a short-reigning pharaoh of Egypt’s Sixth Dynasty, known primarily from sparse archaeological and textual evidence that leaves many details of his rule uncertain.
-
E.
Usera
Usera is a district in the south of Madrid, Spain, known as a largely residential area with a diverse, working-class population.
- 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_69e0b4bf58c081908e52a4500e03ff83 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6b2ee049081909904efe2dc683cd5 |
completed | April 20, 2026, 11:12 p.m. |
Created at: April 16, 2026, 11:43 a.m.