Triple
T655959
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Politburo of the Communist Party of the Soviet Union |
E11648
|
entity |
| Predicate | typicalNumberOfMembers |
P8561
|
FINISHED |
| Object | about 10 to 25 |
—
|
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: about 10 to 25 | Statement: [Politburo of the Communist Party of the Soviet Union, typicalNumberOfMembers, about 10 to 25]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalNumberOfMembers Context triple: [Politburo of the Communist Party of the Soviet Union, typicalNumberOfMembers, about 10 to 25]
-
A.
typicalMembers
Indicates that the related entities are representative or characteristic members of a larger group, category, or class.
-
B.
typicalGroupSizeRange
chosen
Indicates the usual minimum and maximum number of individuals that typically occur together in a group for the given entity.
-
C.
typicalTeamSize
Indicates the usual or most common number of members that make up a given team.
-
D.
minimumNumberOfMembers
Indicates the smallest allowable or required number of members that must be present or involved in a given context or group.
-
E.
originalNumberOfMembers
Indicates the initial total count of members in a group or organization before any changes such as additions or removals.
- 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_69a4932862a0819098be659c814e4981 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49f4d57688190a9515ac494a97b22 |
completed | March 1, 2026, 8:19 p.m. |
| PD | Predicate disambiguation | batch_69a49d121cec81909986c91291bb4ca8 |
completed | March 1, 2026, 8:09 p.m. |
Created at: March 1, 2026, 7:36 p.m.