Triple
T13194353
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Algerian Super Cup |
E314072
|
entity |
| Predicate | matchCountPerEdition |
P108982
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [Algerian Super Cup, matchCountPerEdition, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: matchCountPerEdition Context triple: [Algerian Super Cup, matchCountPerEdition, 1]
-
A.
numberOfEditions
Indicates the total count of distinct editions associated with a given entity.
-
B.
hasOneTimeEdition
Indicates that an entity is associated with a single, non-recurring edition or version that occurs only once.
-
C.
hasDifferentEditions
Indicates that an entity exists in multiple distinct versions or editions that differ in some characteristics.
-
D.
hasEditionIn
Indicates that one entity has a specific edition or version that exists or is available in another entity (such as a particular format, language, or location).
-
E.
appliedToEditionFrequency
Indicates that a particular action, rule, or condition is associated with how often a specific edition occurs or is issued.
- 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_69d806ae1e08819090d95bfe1538cc17 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98cf054f88190b05ced98d5a22a62 |
completed | April 10, 2026, 11:51 p.m. |
| PD | Predicate disambiguation | batch_69d98bc6bc108190b5a6a265bf6e9fd4 |
completed | April 10, 2026, 11:46 p.m. |
| PDg | Predicate description generation | batch_69d98ceeb22c8190a6be666031d9e5a4 |
completed | April 10, 2026, 11:51 p.m. |
Created at: April 9, 2026, 9:16 p.m.