Triple
T1463032
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UNICA – Network of Universities from the Capitals of Europe |
E31556
|
entity |
| Predicate | hasMemberCountApproximate |
P20367
|
FINISHED |
| Object | 50 |
—
|
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: 50 | Statement: [UNICA – Network of Universities from the Capitals of Europe, hasMemberCountApproximate, 50]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMemberCountApproximate Context triple: [UNICA – Network of Universities from the Capitals of Europe, hasMemberCountApproximate, 50]
-
A.
hasApproximateMemberCount
chosen
Indicates that an entity is associated with a group or collection for which only an estimated or non-exact number of members is known.
-
B.
hasCurrentNumberOfMembers
Indicates the current count of members associated with a given entity.
-
C.
hasMemberCountType
Indicates the type or classification used to describe how the number of members in a group or collection is represented.
-
D.
numberOfMembersReturned
Indicates the quantity of members that are provided or yielded as a result of an operation or query.
-
E.
hasMaximumNumberOfMembers
Indicates that there is an upper limit on how many members can be associated with a given 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_69a49917dfc081909acdbdf5d684f1ef |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c5b6e36c81909c47b2f7e66f17d7 |
completed | March 1, 2026, 11:03 p.m. |
| PD | Predicate disambiguation | batch_69a4c48121e48190946c23c583e5fb64 |
completed | March 1, 2026, 10:58 p.m. |
Created at: March 1, 2026, 8 p.m.