Triple
T27430442
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alfred-Georges Gressent |
E690618
|
entity |
| Predicate | laterPoliticalAffiliation |
P114263
|
FINISHED |
| Object | republican circles |
—
|
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: republican circles | Statement: [Alfred-Georges Gressent, laterPoliticalAffiliation, republican circles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: laterPoliticalAffiliation Context triple: [Alfred-Georges Gressent, laterPoliticalAffiliation, republican circles]
-
A.
partnerPoliticalAffiliation
Indicates that one entity has a political affiliation that is associated with, or shared by, its partner entity.
-
B.
holderPoliticalAffiliation
Indicates that a person or officeholder is associated with or belongs to a particular political party or ideology.
-
C.
subjectPoliticalParty
chosen
Indicates the political party with which the subject is formally affiliated or identified.
-
D.
affectedPoliticalParty
Indicates that a political party is impacted or influenced by a particular event, action, policy, or situation.
-
E.
parentPoliticalAffiliation
Indicates that one entity has a political affiliation that is associated with, derived from, or characteristic of their parent.
- 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_69ef52003fb48190b0f1295246182a86 |
completed | April 27, 2026, 12:09 p.m. |
| NER | Named-entity recognition | batch_69fcc4b700748190ae00b21d09c96695 |
completed | May 7, 2026, 4:58 p.m. |
| PD | Predicate disambiguation | batch_69fcb0f9d3d881908a049475182fb039 |
completed | May 7, 2026, 3:34 p.m. |
Created at: April 27, 2026, 12:42 p.m.