Triple
T18984655
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lady Frances Elliot |
E464522
|
entity |
| Predicate | numberOfTimesSpouseWasPrimeMinister |
P29051
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Lady Frances Elliot, numberOfTimesSpouseWasPrimeMinister, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfTimesSpouseWasPrimeMinister Context triple: [Lady Frances Elliot, numberOfTimesSpouseWasPrimeMinister, 2]
-
A.
marriedToPrimeMinisterDuring
Indicates that one person was married to the individual serving as prime minister during a specified time period.
-
B.
spouseNumberOfTermsInOffice
Indicates the number of distinct terms in office that the spouse of the referenced entity has served.
-
C.
marriedToHeadOfGovernmentOf
Indicates that one entity is the spouse of the person who holds the position of head of government of the other entity.
-
D.
spouseOfOfficeholderNumber
Indicates that one entity is the spouse of a specific officeholder identified by their ordinal number in holding a particular office.
-
E.
numberOfTermsAsPrimeMinister
chosen
Indicates how many separate terms an individual has served in the role of prime minister.
- 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_69d8dd008af48190a97ff1c6488edf1b |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d65ec0bc8190b878252b1b4c3620 |
completed | April 20, 2026, 7:31 a.m. |
| PD | Predicate disambiguation | batch_69e4a2f437648190b85650dae8885d48 |
completed | April 19, 2026, 9:40 a.m. |
Created at: April 10, 2026, 12:01 p.m.