Triple
T1536414
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Second Lady of California |
E32558
|
entity |
| Predicate | titleUsage |
P29608
|
FINISHED |
| Object | varies by administration and personal preference of the spouse |
—
|
LITERAL 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: varies by administration and personal preference of the spouse | Statement: [Second Lady of California, titleUsage, varies by administration and personal preference of the spouse]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: titleUsage Context triple: [Second Lady of California, titleUsage, varies by administration and personal preference of the spouse]
-
A.
titleUsedSince
Indicates that a particular title has been in use starting from a specified point in time.
-
B.
usesTitle
Indicates that one entity refers to or addresses another entity using a specific title or formal designation.
-
C.
titles
Indicates that one entity holds a formal title, designation, or name associated with another entity.
-
D.
title
Indicates that one entity serves as the formal name or designation of another entity.
-
E.
titleType
Indicates the specific category or kind of title associated with an entity (e.g., whether it is a main title, alternative title, working title, etc.).
- F. None of above. chosen
Provenance (4 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_69a885ea86308190998f6bc14bb91f8e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a915f323bc8190aa757142c225e0ae |
completed | March 5, 2026, 5:34 a.m. |
| PD | Predicate disambiguation | batch_69a907b046448190be8ea4d7b20255f7 |
completed | March 5, 2026, 4:33 a.m. |
| PDg | Predicate description generation | batch_69a915f1694081908f87b509eda1309f |
completed | March 5, 2026, 5:34 a.m. |
Created at: March 4, 2026, 7:26 p.m.