Triple
T2079410
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UNSC |
E45203
|
entity |
| Predicate | permanentMemberCount |
P11657
|
FINISHED |
| Object | 5 |
—
|
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: 5 | Statement: [UNSC, permanentMemberCount, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: permanentMemberCount Context triple: [UNSC, permanentMemberCount, 5]
-
A.
numberOfNonPermanentMembers
Indicates the count of entities that hold non-permanent (temporary or rotating) membership within a larger group or body.
-
B.
numberOfFullMembers
Indicates the total count of entities that hold full membership status within a specified group or organization.
-
C.
numberOfPermanentMembers
chosen
Indicates the total count of entities that hold permanent membership within a specified group or organization.
-
D.
originalNumberOfMembers
Indicates the initial total count of members in a group or organization before any changes such as additions or removals.
-
E.
previousNumberOfMembers
Indicates the number of members an entity had at an earlier or prior point in time.
- 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_69a8891869c88190a02643e3bb746f59 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abba3307308190ab329fe3192b2e0f |
completed | March 7, 2026, 5:40 a.m. |
| PD | Predicate disambiguation | batch_69abb7b298a48190b4bdf7c9800b058d |
completed | March 7, 2026, 5:29 a.m. |
Created at: March 4, 2026, 7:41 p.m.