Triple
T48557
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Social Security Board |
E953
|
entity |
| Predicate | numberOfBoardMembers |
P3128
|
FINISHED |
| Object | 3 |
—
|
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: 3 | Statement: [Social Security Board, numberOfBoardMembers, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfBoardMembers Context triple: [Social Security Board, numberOfBoardMembers, 3]
-
A.
numberOfParticipants
Indicates the total count of entities involved in a particular event, activity, or relationship.
-
B.
numberOfPositions
Indicates the total count of distinct positions or roles associated with a given entity.
-
C.
hasNumberOfMemberInstitutions
Indicates the quantitative count of member institutions associated with a given entity.
-
D.
numberOfPersons
Indicates the total count of individual persons associated with or involved in a given entity, event, or context.
-
E.
additionalMembersAllowed
Indicates that more members are permitted to be added beyond those already included or initially specified.
- 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_69a2480baefc81909951b14058479aa2 |
completed | Feb. 28, 2026, 1:42 a.m. |
| NER | Named-entity recognition | batch_69a24c1a5c14819088748317a3f262c8 |
completed | Feb. 28, 2026, 1:59 a.m. |
| PD | Predicate disambiguation | batch_69a24abe7cb481908d969e54032f6c75 |
completed | Feb. 28, 2026, 1:54 a.m. |
| PDg | Predicate description generation | batch_69a24c198e74819088a211001d0b54d4 |
completed | Feb. 28, 2026, 1:59 a.m. |
Created at: Feb. 28, 2026, 1:47 a.m.