Triple
T5594279
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A People’s History of the United States |
E146955
|
entity |
| Predicate | hasChapterOn |
P32494
|
FINISHED |
| Object | Columbus’s arrival in the Americas |
—
|
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: Columbus’s arrival in the Americas | Statement: [A People’s History of the United States, hasChapterOn, Columbus’s arrival in the Americas]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasChapterOn Context triple: [A People’s History of the United States, hasChapterOn, Columbus’s arrival in the Americas]
-
A.
containsChapter
Indicates that one entity (typically a larger work or document) includes another entity as a chapter within its structure.
-
B.
hadChapterOf
Indicates that an entity (such as a book or document) includes or contains a specific chapter as one of its parts.
-
C.
chapterOn
chosen
Indicates that one entity (typically a chapter) is about, discusses, or focuses on the subject represented by another entity.
-
D.
hasLocalChaptersIn
Indicates that an organization maintains one or more local chapters or branches within a specified geographic area or location.
-
E.
chapterType
Indicates the specific kind or category of a chapter within a larger work or document.
- 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_69c009036c408190981a8d690b679b67 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c020bc41408190bc990da8ddeb931e |
completed | March 22, 2026, 5:02 p.m. |
| PD | Predicate disambiguation | batch_69c01b16b9bc8190ab0b945507d90e05 |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:38 p.m.