Triple
T29888757
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lena Moi |
E759086
|
entity |
| Predicate | spouseTermStartOfPresident |
P86863
|
FINISHED |
| Object | Daniel arap Moi’s presidency (1978) |
—
|
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: Daniel arap Moi’s presidency (1978) | Statement: [Lena Moi, spouseTermStartOfPresident, Daniel arap Moi’s presidency (1978)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouseTermStartOfPresident Context triple: [Lena Moi, spouseTermStartOfPresident, Daniel arap Moi’s presidency (1978)]
-
A.
spousePositionHeldStartTime
Indicates the date and time when a spouse first began holding a particular position or office.
-
B.
marriedToUSPresident
Indicates being legally married to an individual who holds or has held the office of President of the United States.
-
C.
spouseNumberOfTermsInOffice
Indicates the number of distinct terms in office that the spouse of the referenced entity has served.
-
D.
marriedToDuringOffice
chosen
Indicates that one person was married to another person specifically during the time they held a particular office or position.
-
E.
spouseOrdinalNumberAsPresident
Indicates the numerical order in which a person’s spouse served as president (e.g., first, second, third).
- 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_69f2245de2f48190a481404896b56254 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69fba78aca4c8190b8f1831e8cc04e06 |
completed | May 6, 2026, 8:41 p.m. |
| PD | Predicate disambiguation | batch_69fba34a65a4819088bac6c17542d71c |
completed | May 6, 2026, 8:23 p.m. |
Created at: April 29, 2026, 6:01 p.m.