Triple
T1377516
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alan Kay |
E29258
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Kay |
E49538
|
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: Kay | Statement: [Alan Kay, familyName, Kay]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kay Context triple: [Alan Kay, familyName, Kay]
-
A.
Kay
chosen
Kay is a common diminutive or nickname for the given name Catherine.
-
B.
Bakke
Bakke is the commonly used shorthand name for the landmark U.S. Supreme Court case Regents of the University of California v. Bakke, which addressed the constitutionality of race-based admissions policies in higher education.
-
C.
Verna
Verna is a feminine given name that gained particular recognition through film editor Verna Fields, known for her work on movies like "Jaws."
-
D.
Douglas
Douglas is a masculine given name of Scottish origin that has been widely used in English-speaking countries.
-
E.
Douglas
Douglas is a small lakeside city in Allegan County, Michigan, known for its arts community and proximity to Lake Michigan beaches.
- 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_69a498d883a48190bfdca525296ef7ee |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c3173548819082aec7c1af9c577c |
completed | March 1, 2026, 10:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acd488698481909411c004aadfcaec |
completed | March 8, 2026, 1:44 a.m. |
Created at: March 1, 2026, 7:59 p.m.