Triple
T362154
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Surgeon General’s Report on Smoking and Health |
E7878
|
entity |
| Predicate | numberOfCommitteeMembers |
P12377
|
FINISHED |
| Object | 10 |
—
|
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: 10 | Statement: [Surgeon General’s Report on Smoking and Health, numberOfCommitteeMembers, 10]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfCommitteeMembers Context triple: [Surgeon General’s Report on Smoking and Health, numberOfCommitteeMembers, 10]
-
A.
numberOfMainCommittees
Indicates the total count of primary or main committees associated with an entity.
-
B.
numberOfBoardMembers
Indicates the total count of individuals who serve as members on a board.
-
C.
selectionCommitteeSize
Indicates the number of members that make up a given selection committee.
-
D.
numberOfSenators
Indicates the total count of senators associated with a given political body, region, or entity.
-
E.
totalNumberOfLegislators
Indicates the total count of legislators associated with a given political body, jurisdiction, or legislative session.
- 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_69a2e7e880008190a6ad7e06e5d03007 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ebcfb0f48190b9a9010c7837ac58 |
completed | Feb. 28, 2026, 1:21 p.m. |
| PD | Predicate disambiguation | batch_69a2e95c843c8190b2aba9af6e869ba1 |
completed | Feb. 28, 2026, 1:10 p.m. |
| PDg | Predicate description generation | batch_69a2ea2c44408190946267525c88e811 |
completed | Feb. 28, 2026, 1:14 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.