Triple
T22418131
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hampton-Tuskegee model of industrial education |
E554174
|
entity |
| Predicate | wasInfluentialInPeriod |
P56906
|
FINISHED |
| Object | early 20th century |
—
|
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: early 20th century | Statement: [Hampton-Tuskegee model of industrial education, wasInfluentialInPeriod, early 20th century]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wasInfluentialInPeriod Context triple: [Hampton-Tuskegee model of industrial education, wasInfluentialInPeriod, early 20th century]
-
A.
hadInfluenceOn
Indicates that one entity affected, shaped, or contributed to the development, behavior, or characteristics of another entity.
-
B.
centuryOfGreatestInfluence
chosen
Indicates the century during which an entity exerted its greatest impact or influence.
-
C.
hasHistoricalWritingInfluenceFrom
Indicates that one entity’s historical writing style, content, or traditions are influenced by those of another entity.
-
D.
hasHistoricalInfluenceFrom
Indicates that one entity’s characteristics, development, or significance have been shaped or affected by the past actions, ideas, or legacy of another entity.
-
E.
hasEnduringInfluenceOn
Indicates that one entity exerts a lasting, long-term impact on another entity’s state, development, or behavior.
- 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_69e11e4e6ce8819085a1e06d886bf21c |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15947dcc08190a584636f87669316 |
completed | April 29, 2026, 1:05 a.m. |
| PD | Predicate disambiguation | batch_69e8989495bc81909d2699fce5992e28 |
completed | April 22, 2026, 9:44 a.m. |
Created at: April 16, 2026, 8:46 p.m.