Triple
T2427885
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 東北大学 |
E53572
|
entity |
| Predicate | 重点分野 |
P19540
|
FINISHED |
| Object | 材料科学 |
—
|
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: 材料科学 | Statement: [東北大学, 重点分野, 材料科学]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 重点分野 Context triple: [東北大学, 重点分野, 材料科学]
-
A.
focusType
Indicates the specific kind or category of focus or attention that is being applied to or associated with an entity or interaction.
-
B.
keyIssueArea
chosen
Indicates that something is a primary topic, domain, or field that is central or especially important within a broader context or discussion.
-
C.
featuresTopic
Indicates that something (such as a work, event, or item) prominently includes, focuses on, or is organized around a particular topic.
-
D.
focusesBy
Indicates that one entity directs its attention, effort, or emphasis toward another entity or specific aspect of it.
-
E.
primaryTopicOf
Indicates that a given subject is the main or central topic described by another resource (such as a document, page, or record).
- 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_69ab495c44d48190b7235b23719bc3f6 |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abcc74a5108190a3a9631b0cc1a127 |
completed | March 7, 2026, 6:57 a.m. |
| PD | Predicate disambiguation | batch_69abc5aa1b60819081b87f7985c6cff3 |
completed | March 7, 2026, 6:28 a.m. |
Created at: March 6, 2026, 9:42 p.m.