Triple
T16243819
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harriet Malvina Howe |
E394317
|
entity |
| Predicate | ordinalVicePresidentNumberOfSpouse |
P122313
|
FINISHED |
| Object | 18 |
—
|
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: 18 | Statement: [Harriet Malvina Howe, ordinalVicePresidentNumberOfSpouse, 18]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ordinalVicePresidentNumberOfSpouse Context triple: [Harriet Malvina Howe, ordinalVicePresidentNumberOfSpouse, 18]
-
A.
spouseOrdinalNumberAsPresident
Indicates the numerical order in which a person’s spouse served as president (e.g., first, second, third).
-
B.
vicePresidentNumber
Indicates the ordinal position or numerical designation of an individual serving in the role of vice president within a given organization or context.
-
C.
firstVicePresidentialOccupant
Indicates that the subject is the very first individual to hold the office of vice president for the specified entity or position.
-
D.
marriedToVicePresident
Indicates that one person is legally married to an individual who holds the office or role of vice president.
-
E.
spouseNumberOfTermsInOffice
Indicates the number of distinct terms in office that the spouse of the referenced entity has served.
- 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_69d87f2171208190951025e526947816 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e24560060c8190ace4f4c0bd0d886d |
completed | April 17, 2026, 2:36 p.m. |
| PD | Predicate disambiguation | batch_69e219ee6f6481909663b388dc99770a |
completed | April 17, 2026, 11:30 a.m. |
| PDg | Predicate description generation | batch_69e21e55a2388190b29a045a8c608ba4 |
completed | April 17, 2026, 11:49 a.m. |
Created at: April 10, 2026, 5:04 a.m.