Triple
T22219493
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laurie Kenyon |
E549170
|
entity |
| Predicate | hasAlias |
P455
|
FINISHED |
| Object | Kate |
—
|
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: Kate | Statement: [Laurie Kenyon, hasAlias, Kate]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kate Context triple: [Laurie Kenyon, hasAlias, Kate]
-
A.
Kate
chosen
Kate is a common diminutive form of the given name Catherine, frequently used in English-speaking countries.
-
B.
Kate
Kate is the Allied reporting name for the Nakajima B5N, a Japanese World War II carrier-based torpedo bomber aircraft.
-
C.
Anne
Anne was the ship on which the 17th-century English sailor and later Ceylon captive Robert Knox served during his voyages.
-
D.
Anne
Anne of Palatinate-Simmern was a 16th-century German noblewoman from the House of Wittelsbach who became Electress Palatine through marriage to Elector Frederick III.
-
E.
Anne
Anne is the central, imaginative orphan protagonist of the classic novel "Anne of Green Gables," known for her vivid personality and red hair.
- 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_69e11e403d6481909a94d0aaf157f6ef |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12b8edd288190a49f10e009122057 |
completed | April 28, 2026, 9:50 p.m. |
Created at: April 16, 2026, 8:37 p.m.