Triple
T12049275
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Helen Willis |
E286870
|
entity |
| Predicate | hasLastName |
P18
|
FINISHED |
| Object | Willis |
E31141
|
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: Willis | Statement: [Helen Willis, hasLastName, Willis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Willis Context triple: [Helen Willis, hasLastName, Willis]
-
A.
Willis
chosen
Willis is a masculine given name and surname of English origin, often considered a variant or cognate of the name Wilson.
-
B.
Sullivan
Sullivan is a common Irish surname that has been borne by numerous notable figures in politics, arts, sports, and other fields.
-
C.
Sullivan
Sullivan is a shortened name for the international law firm Sullivan & Worcester LLP, known for its corporate, tax, and financial legal services.
-
D.
Sullivan
Sullivan is a town in Madison County, New York, known for its rural character and proximity to Chittenango and Oneida Lake.
-
E.
Wayne
Wayne is a suburban community in Pennsylvania’s Main Line region, known for its residential neighborhoods and commuter access to Philadelphia.
- 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_69d6ab4780948190bdb9f7620c2ac27e |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d904227958819084dbd5eb2566c735 |
completed | April 10, 2026, 2:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f49dd140a48190844f64c228e6367a |
completed | May 1, 2026, 12:34 p.m. |
Created at: April 8, 2026, 9:47 p.m.