Triple
T875540
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | International Classification of Primary Care |
E18909
|
entity |
| Predicate | chapterType |
P20981
|
FINISHED |
| Object | body systems and problem areas |
—
|
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: body systems and problem areas | Statement: [International Classification of Primary Care, chapterType, body systems and problem areas]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: chapterType Context triple: [International Classification of Primary Care, chapterType, body systems and problem areas]
-
A.
containsChapter
Indicates that one entity (typically a larger work or document) includes another entity as a chapter within its structure.
-
B.
book5Content
Indicates that one entity is the content or textual material contained within the book represented by the other entity.
-
C.
book7Content
Indicates that an entity contains or represents the content of book 7 in a series or collection.
-
D.
chapterNumber
Indicates the specific ordinal position a chapter occupies within a larger ordered work, such as a book or document.
-
E.
numberOfChapters
Indicates the total count of chapters associated with a given entity.
- 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_69a4938db1f081909bcd1ad2713b6096 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4acae12948190923d31966c26a130 |
completed | March 1, 2026, 9:16 p.m. |
| PD | Predicate disambiguation | batch_69a4aa8d47c081909b02a53e305ccf7a |
completed | March 1, 2026, 9:07 p.m. |
| PDg | Predicate description generation | batch_69a4ab9634948190b25ea1b2e34df87d |
completed | March 1, 2026, 9:11 p.m. |
Created at: March 1, 2026, 7:39 p.m.