Triple
T1613
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | National Academy of Sciences |
E30
|
entity |
| Predicate | focusArea |
P3
|
FINISHED |
| Object | science policy |
—
|
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: science policy | Statement: [National Academy of Sciences, focusArea, science policy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: focusArea Context triple: [National Academy of Sciences, focusArea, science policy]
-
A.
focusesOn
Indicates that one entity directs its attention, effort, or primary activity toward another entity or specific subject.
-
B.
focusPeriod
Indicates the specific time span during which attention, activity, or analysis is concentrated on something.
-
C.
area
Indicates that one entity has a measured two-dimensional extent or surface size quantified by another entity.
-
D.
fieldOfWork
chosen
Indicates the professional or academic domain in which an entity is primarily engaged or specializes.
-
E.
areaServed
Indicates the geographic region or jurisdiction within which a service, organization, or activity is provided or applicable.
- 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_69a22a285828819081a58308fb963df1 |
completed | Feb. 27, 2026, 11:35 p.m. |
| NER | Named-entity recognition | batch_69a23344daf8819083118bbac5f46568 |
completed | Feb. 28, 2026, 12:13 a.m. |
| PD | Predicate disambiguation | batch_69a232e52e7c81909c072703e28e8c61 |
completed | Feb. 28, 2026, 12:12 a.m. |
Created at: Feb. 27, 2026, 11:36 p.m.