Triple
T23541245
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Counts Leslie (Holy Roman Empire) |
E577752
|
entity |
| Predicate | influencedIn |
P152735
|
FINISHED |
| Object | Holy Roman Empire politics |
—
|
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: Holy Roman Empire politics | Statement: [Counts Leslie (Holy Roman Empire), influencedIn, Holy Roman Empire politics]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: influencedIn Context triple: [Counts Leslie (Holy Roman Empire), influencedIn, Holy Roman Empire politics]
-
A.
influencedPerson
Indicates that one entity has affected, shaped, or guided the thoughts, behavior, or development of another person.
-
B.
wereInfluencedBy
Indicates that one entity’s ideas, actions, or characteristics were shaped or affected by another entity.
-
C.
influenced
Indicates that one entity has affected, shaped, or altered another entity’s state, behavior, or characteristics.
-
D.
influentialFrom
Indicates that one entity has exerted influence on another, contributing to or shaping the latter’s ideas, behavior, or development.
-
E.
hadInfluenceOn
Indicates that one entity affected, shaped, or contributed to the development, behavior, or characteristics of another entity.
- 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_69e245f9d5d08190a4a20004e1784e20 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f1ae1b3a8c8190b5b6a58f0476c5d2 |
completed | April 29, 2026, 7:07 a.m. |
| PD | Predicate disambiguation | batch_69f118afabd88190bd88f49597d120e8 |
completed | April 28, 2026, 8:29 p.m. |
| PDg | Predicate description generation | batch_69f121cc494081908c987adfcde89b0e |
completed | April 28, 2026, 9:08 p.m. |
Created at: April 17, 2026, 6:10 p.m.