Triple
T197853
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guns, Germs, and Steel |
E4036
|
entity |
| Predicate | partTitle |
P2765
|
FINISHED |
| Object | From Eden to Cajamarca |
—
|
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: From Eden to Cajamarca | Statement: [Guns, Germs, and Steel, partTitle, From Eden to Cajamarca]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: partTitle Context triple: [Guns, Germs, and Steel, partTitle, From Eden to Cajamarca]
-
A.
title
Indicates that one entity serves as the formal name or designation of another entity.
-
B.
subtitle
chosen
Indicates that one work serves as a secondary or explanatory title to another, typically appearing beneath the main title.
-
C.
titleType
Indicates the specific category or kind of title associated with an entity (e.g., whether it is a main title, alternative title, working title, etc.).
-
D.
titleInEnglish
Indicates that an entity’s title or name is given in the English language.
-
E.
partBType
Indicates that one entity is classified as the type or category to which the second entity (part B) belongs.
- 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_69a254bca59881909a15e1496f1508c7 |
completed | Feb. 28, 2026, 2:36 a.m. |
| NER | Named-entity recognition | batch_69a25be47ea881909c296b30a0d47a65 |
completed | Feb. 28, 2026, 3:07 a.m. |
| PD | Predicate disambiguation | batch_69a25b47481c8190add47c641c977bb9 |
completed | Feb. 28, 2026, 3:04 a.m. |
Created at: Feb. 28, 2026, 2:44 a.m.