Triple
T21995
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Autobiography of Andrew Carnegie |
E437
|
entity |
| Predicate | explains |
P264
|
FINISHED |
| Object | Carnegie's belief in using wealth for the public good |
—
|
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: Carnegie's belief in using wealth for the public good | Statement: [Autobiography of Andrew Carnegie, explains, Carnegie's belief in using wealth for the public good]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: explains Context triple: [Autobiography of Andrew Carnegie, explains, Carnegie's belief in using wealth for the public good]
-
A.
describes
chosen
Indicates that one entity provides an explanation, representation, or account of another entity or concept.
-
B.
advises
Indicates that one entity provides guidance, recommendations, or counsel to another entity.
-
C.
analyzes
Indicates that one entity systematically examines or evaluates another entity to understand its nature, structure, or components.
-
D.
integrates
Indicates that one entity combines or brings together another entity or set of entities into a unified, functioning whole.
-
E.
educates
Indicates that one entity provides instruction, knowledge, or training to another entity.
- 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_69a243b4ac2c8190b93c303df797b7b2 |
completed | Feb. 28, 2026, 1:24 a.m. |
| NER | Named-entity recognition | batch_69a246e94ca881908f7a7d2c0b293033 |
completed | Feb. 28, 2026, 1:37 a.m. |
| PD | Predicate disambiguation | batch_69a24654724481909ba14b7f68d2a472 |
completed | Feb. 28, 2026, 1:35 a.m. |
Created at: Feb. 28, 2026, 1:34 a.m.