Triple
T10371757
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TFEU Article 102 |
E244399
|
entity |
| Predicate | exampleOfAbuse |
P36524
|
FINISHED |
| Object | imposing unfair purchase or selling prices |
—
|
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: imposing unfair purchase or selling prices | Statement: [TFEU Article 102, exampleOfAbuse, imposing unfair purchase or selling prices]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: exampleOfAbuse Context triple: [TFEU Article 102, exampleOfAbuse, imposing unfair purchase or selling prices]
-
A.
abusePubliclyExposed
Indicates that one entity mistreats, exploits, or harms another entity whose sensitive information or vulnerabilities have been made publicly accessible.
-
B.
typeOfAbuse
chosen
Indicates the specific kind or category of abusive behavior that one entity inflicts on another.
-
C.
abusePolicy
Indicates that one entity enforces or is governed by rules or guidelines defining unacceptable abusive behavior toward others.
-
D.
periodOfAbuse
Indicates the time span during which abusive behavior occurred or was experienced.
-
E.
abuseSurvivorOf
Indicates that one entity has previously suffered abuse perpetrated by the other entity.
- 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_69d381b3e328819094b23b8edcd29b5a |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e97ed09c8190a3627aa7b5eea62f |
completed | April 7, 2026, 11:24 a.m. |
| PD | Predicate disambiguation | batch_69d4dface5508190a7b42f01ad0a19a2 |
completed | April 7, 2026, 10:42 a.m. |
Created at: April 6, 2026, 12:01 p.m.