Triple
T8996053
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dorothy Yorke |
E214913
|
entity |
| Predicate | spouseNumberOfTermsInOffice |
P86222
|
FINISHED |
| Object | multiple terms |
—
|
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: multiple terms | Statement: [Dorothy Yorke, spouseNumberOfTermsInOffice, multiple terms]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouseNumberOfTermsInOffice Context triple: [Dorothy Yorke, spouseNumberOfTermsInOffice, multiple terms]
-
A.
spouseLaterOffice
Indicates that one person’s spouse held a particular office or position at a later time than the person in question.
-
B.
spouseOffice
Indicates that one entity holds an office or position that is associated with, or held by, the spouse of another entity.
-
C.
spouseOrdinalNumberAsPresident
Indicates the numerical order in which a person’s spouse served as president (e.g., first, second, third).
-
D.
spouseCount
Indicates the number of spouses an entity has.
-
E.
roleDuringSpouseTenure
Indicates that a person held a particular role or position specifically during the period when their spouse was in office or serving in a defined tenure.
- 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_69ca83a05c608190bdfdbdb25e994b39 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc68df33c48190a5017426e59c0bc4 |
completed | April 1, 2026, 12:37 a.m. |
| PD | Predicate disambiguation | batch_69cc5edba0f88190b97401636a076d7a |
completed | March 31, 2026, 11:55 p.m. |
| PDg | Predicate description generation | batch_69cc5febd0a08190b2de6fb422343001 |
completed | March 31, 2026, 11:59 p.m. |
Created at: March 30, 2026, 7:04 p.m.