Triple
T9883992
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Philip M. Morse |
E180882
|
entity |
| Predicate | appliedScienceTo |
P83724
|
FINISHED |
| Object | military logistics |
—
|
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: military logistics | Statement: [Philip M. Morse, appliedScienceTo, military logistics]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: appliedScienceTo Context triple: [Philip M. Morse, appliedScienceTo, military logistics]
-
A.
appliesResearchTo
chosen
Indicates that an entity uses or implements research findings, methods, or insights in relation to another entity, context, or problem.
-
B.
hasScience
Indicates that an entity possesses, includes, or is associated with a particular scientific discipline, content, or attribute.
-
C.
appliedPrimarilyTo
Indicates that something is used mainly or chiefly in relation to a particular target, context, or purpose, rather than being used broadly or equally elsewhere.
-
D.
innovationArea
Indicates the thematic or domain-specific field in which an innovation is focused or applied.
-
E.
scientificInstruments
Indicates that one entity is a scientific instrument used by, associated with, or relevant to another entity in the context of scientific measurement or research.
- 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_69ca828082cc8190a40f8d299caa6545 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cdb453b388819095a5070399d9788d |
completed | April 2, 2026, 12:12 a.m. |
| PD | Predicate disambiguation | batch_69cd1d810ed48190a252b70e9390c8f3 |
completed | April 1, 2026, 1:28 p.m. |
Created at: March 30, 2026, 8:38 p.m.