Triple
T36763428
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eva Luise Köhler |
E908269
|
entity |
| Predicate | marriedToPresidentOf |
P195427
|
FINISHED |
| Object | Germany |
—
|
NE NERFINISHED |
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: Germany | Statement: [Eva Luise Köhler, marriedToPresidentOf, Germany]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marriedToPresidentOf Context triple: [Eva Luise Köhler, marriedToPresidentOf, Germany]
-
A.
marriedToUSPresident
Indicates being legally married to an individual who holds or has held the office of President of the United States.
-
B.
marriedToVicePresident
Indicates that one person is legally married to an individual who holds the office or role of vice president.
-
C.
marriedToFuturePresident
Indicates being married to someone who will later become president.
-
D.
marriedToAFormerFirstLadyOfTheUnitedStates
Indicates that a person is or was married to someone who previously held the role of First Lady of the United States.
-
E.
marriedToDuringOffice
Indicates that one person was married to another person specifically during the time they held a particular office or position.
- 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_69f76e786ba481909cdcf6cf6b39dd32 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fdd07a34c08190982b8c61c2775cf6 |
completed | May 8, 2026, noon |
| PD | Predicate disambiguation | batch_69fdbd25c7908190b72fca8de7ce503f |
completed | May 8, 2026, 10:38 a.m. |
| PDg | Predicate description generation | batch_69fdd07724f88190a33ec602642d2ea3 |
completed | May 8, 2026, noon |
Created at: May 3, 2026, 4:12 p.m.