Triple
T18353647
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lee Hays |
E439733
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Hays |
—
|
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: Hays | Statement: [Lee Hays, familyName, Hays]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hays Context triple: [Lee Hays, familyName, Hays]
-
A.
Hays
chosen
Hays is a surname most notably associated with Will H. Hays, the American politician and film industry figure behind the early Hollywood production code.
-
B.
Hays, Kansas
Hays, Kansas is a regional city in northwestern Kansas known as a commercial, educational, and medical hub, home to Fort Hays State University and rich frontier history.
-
C.
Hays, Texas
Hays, Texas is a small unincorporated community located within Hays County in central Texas.
-
D.
Haysville, Kansas
Haysville, Kansas is a small suburban city within the Wichita metropolitan area known for its residential character and community-focused atmosphere.
-
E.
Hutchinson
Hutchinson is a common English surname borne by numerous notable individuals across fields such as science, politics, and the arts.
- 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_69d8b918221c8190a9f7b563d64ac677 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e516d37bc08190abc9b4e8c937c914 |
completed | April 19, 2026, 5:54 p.m. |
Created at: April 10, 2026, 10:37 a.m.