Triple
T847436
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | European Parliament (Strasbourg seat) |
E18306
|
entity |
| Predicate | primaryUseFrequency |
P16914
|
FINISHED |
| Object | monthly plenary sessions |
—
|
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: monthly plenary sessions | Statement: [European Parliament (Strasbourg seat), primaryUseFrequency, monthly plenary sessions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryUseFrequency Context triple: [European Parliament (Strasbourg seat), primaryUseFrequency, monthly plenary sessions]
-
A.
usedPrimarilyIn
Indicates that something is mainly or most commonly employed within a particular context, domain, or purpose.
-
B.
primaryMode
Indicates the main or most commonly used method, manner, or form in which an action, process, or interaction is carried out between entities.
-
C.
majorUse
Indicates that something serves as the primary or most significant use or application of an entity.
-
D.
usageType
Indicates the specific manner, purpose, or context in which something is used or intended to be used.
-
E.
performedFrequency
chosen
Indicates how often an action or activity is carried out within a given time period.
- 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_69a4938b04208190b82e1df6b572c548 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ac0ba6b4819089c15ed7e1765502 |
completed | March 1, 2026, 9:13 p.m. |
| PD | Predicate disambiguation | batch_69a4aa807adc8190ad808a573cf8e923 |
completed | March 1, 2026, 9:07 p.m. |
Created at: March 1, 2026, 7:38 p.m.