Triple
T30548803
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kisses for My President |
E777495
|
entity |
| Predicate | portraysFictionalOfficeHolder |
P49777
|
FINISHED |
| Object | first female U.S. president |
—
|
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: first female U.S. president | Statement: [Kisses for My President, portraysFictionalOfficeHolder, first female U.S. president]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portraysFictionalOfficeHolder Context triple: [Kisses for My President, portraysFictionalOfficeHolder, first female U.S. president]
-
A.
portraysOffice
Indicates that one entity depicts, represents, or shows an office (as a place, role, or position) associated with another entity.
-
B.
portraysFictionalEntity
Indicates that one entity depicts, represents, or plays the role of a fictional character or figure.
-
C.
depictsOfficeHolder
Indicates that one entity is a depiction (such as an image or representation) of a person in their capacity as an office holder of a specific position.
-
D.
portraysUSPresident
chosen
Indicates that one entity depicts, represents, or plays the role of a U.S. President in some medium or context.
-
E.
hasFictionalOffice
Indicates that one entity maintains or is associated with an office or workplace that exists only in a fictional or imaginary context.
- 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_69f2249e19108190a458ab446096bf22 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69fd7fdafbe881908a31fcb407af2c34 |
completed | May 8, 2026, 6:16 a.m. |
| PD | Predicate disambiguation | batch_69fd7ef0ea908190b5d83f71565bdb1c |
completed | May 8, 2026, 6:13 a.m. |
Created at: April 29, 2026, 8:20 p.m.