Triple
T447420
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | An Essay Concerning Human Understanding |
E7051
|
entity |
| Predicate | book4MainTopic |
P7040
|
FINISHED |
| Object | knowledge and opinion |
—
|
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: knowledge and opinion | Statement: [An Essay Concerning Human Understanding, book4MainTopic, knowledge and opinion]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: book4MainTopic Context triple: [An Essay Concerning Human Understanding, book4MainTopic, knowledge and opinion]
-
A.
primaryTopicOf
Indicates that a given subject is the main or central topic described by another resource (such as a document, page, or record).
-
B.
containsBook
Indicates that one entity (typically a container or collection) includes a specific book as part of its contents.
-
C.
libraryType
Indicates the specific category or classification of a library based on its function, scope, or organizational role.
-
D.
subjectMatter
Indicates the topic, theme, or content area that something (such as a work, document, or discussion) is about.
-
E.
subjectOfWork
chosen
Indicates that one entity is the main topic, focus, or theme that a particular work (such as a book, article, or artwork) is about.
- 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_69a2e7e4676c81909ea0dbdecac0687c |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ef6429e881908aa758da64299a16 |
completed | Feb. 28, 2026, 1:36 p.m. |
| PD | Predicate disambiguation | batch_69a2eddfb5508190a4e06e1b260d8b2b |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.