Triple
T2140403
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 116th United States Congress |
E46746
|
entity |
| Predicate | numberOfResidentCommissioners |
P37054
|
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: [116th United States Congress, numberOfResidentCommissioners, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfResidentCommissioners Context triple: [116th United States Congress, numberOfResidentCommissioners, 1]
-
A.
numberOfCommissioners
Indicates the specific count of commissioners associated with a given entity or context.
-
B.
numberOfCommitteeMembers
Indicates the total count of individuals who are members of a given committee.
-
C.
numberOfRepresentatives
Indicates the quantity of representatives associated with a given entity or unit.
-
D.
hasNumberOfCouncillors
Indicates the relationship that specifies how many councillors are associated with a given entity.
-
E.
numberOfColoniesRepresented
Indicates the count of distinct colonies that are represented or involved in relation to a given entity or context.
- 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_69a88a174ab48190a5db20c132e5dccf |
completed | March 4, 2026, 7:37 p.m. |
| NER | Named-entity recognition | batch_69abbf74147c81908793c3694894f94a |
completed | March 7, 2026, 6:02 a.m. |
| PD | Predicate disambiguation | batch_69abbd96a3b0819081efbfef975e1513 |
completed | March 7, 2026, 5:54 a.m. |
| PDg | Predicate description generation | batch_69abbf71edf08190add69022aabfd49d |
completed | March 7, 2026, 6:02 a.m. |
Created at: March 4, 2026, 7:44 p.m.