Triple
T10192733
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sheila Kelley |
E238078
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Kelley |
E289986
|
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: Kelley | Statement: [Sheila Kelley, familyName, Kelley]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kelley Context triple: [Sheila Kelley, familyName, Kelley]
-
A.
Kelley
chosen
Kelley is a surname most notably associated with Florence Kelley, a prominent American social and political reformer who fought for labor rights and child welfare in the late 19th and early 20th centuries.
-
B.
Kelsey
Kelsey is a given name most famously associated with American actor and comedian Kelsey Grammer.
-
C.
Keally
Keally is a surname most notably associated with Francis Keally, an American architect active in the early to mid-20th century.
-
D.
Kelli
Kelli is a feminine given name, typically considered a variant spelling of Kelly.
-
E.
Keller
Keller is a suburban city in the Dallas–Fort Worth metropolitan area known for its family-friendly neighborhoods and strong public schools.
- 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_69ca84de1b208190bf17bb305b002605 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdedc4fb808190aae2e4b84be96f83 |
completed | April 2, 2026, 4:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d317ca2cf481909cf715ef9248be3c |
completed | April 6, 2026, 2:17 a.m. |
Created at: March 30, 2026, 9:13 p.m.