Triple
T24377250
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | No Way to Treat a First Lady |
E614509
|
entity |
| Predicate | hasFictionalUSPresident |
P155971
|
FINISHED |
| Object | philandering husband of the First Lady |
—
|
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: philandering husband of the First Lady | Statement: [No Way to Treat a First Lady, hasFictionalUSPresident, philandering husband of the First Lady]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalUSPresident Context triple: [No Way to Treat a First Lady, hasFictionalUSPresident, philandering husband of the First Lady]
-
A.
hasFictionalLeader
Indicates that an entity is led or governed by a leader who is a fictional character rather than a real person.
-
B.
hasFictionalSpokesperson
Indicates that an entity is represented or promoted by a spokesperson who is a fictional or imaginary character.
-
C.
hasPresident
Indicates that an entity holds the position or role of president for another entity.
-
D.
portraysUSPresident
Indicates that one entity depicts, represents, or plays the role of a U.S. President in some medium or context.
-
E.
onlyPresident
Indicates that the subject is the sole individual holding the role of president within a given context or organization.
- 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_69e2d7e1e010819098b95eb3f905943d |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f293d7fb188190bfab5e7ff83fa884 |
completed | April 29, 2026, 11:27 p.m. |
| PD | Predicate disambiguation | batch_69f287bb1b2c81909c2e7fcc392ad143 |
completed | April 29, 2026, 10:35 p.m. |
| PDg | Predicate description generation | batch_69f28f4d978c81908310c01def2514cc |
completed | April 29, 2026, 11:07 p.m. |
Created at: April 18, 2026, 2:02 a.m.