Triple
T14242329
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Viscountess Beaconsfield |
E353040
|
entity |
| Predicate | titleHolderSpousePositionCount |
P86222
|
FINISHED |
| Object | twice |
—
|
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: twice | Statement: [Viscountess Beaconsfield, titleHolderSpousePositionCount, twice]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: titleHolderSpousePositionCount Context triple: [Viscountess Beaconsfield, titleHolderSpousePositionCount, twice]
-
A.
spouseCount
Indicates the number of spouses an entity has.
-
B.
positionHeldBySpouse
Indicates that a particular position, role, or office is or was held by the spouse of a given person.
-
C.
spouseNumberOfTermsInOffice
chosen
Indicates the number of distinct terms in office that the spouse of the referenced entity has served.
-
D.
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.
-
E.
currentTitleHolderSpouseOf
Indicates that one entity is the current spouse of the individual who presently holds a specified title.
- F. None of above.
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_69d8278adc7c8190a9218d69bce3c4e6 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de6244ad188190b9d9db7914240410 |
completed | April 14, 2026, 3:50 p.m. |
| PD | Predicate disambiguation | batch_69de05bf069c8190b69f00f00f5eb126 |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 10, 2026, 1:08 a.m.