Triple
T33621589
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Robert J. Sampson |
E861282
|
entity |
| Predicate | hasInfluencedTheory |
P45664
|
FINISHED |
| Object | refinement of broken windows theory |
—
|
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: refinement of broken windows theory | Statement: [Robert J. Sampson, hasInfluencedTheory, refinement of broken windows theory]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasInfluencedTheory Context triple: [Robert J. Sampson, hasInfluencedTheory, refinement of broken windows theory]
-
A.
hasInfluenceOnDiscipline
Indicates that one entity exerts an effect, shaping force, or contributing impact on the development, direction, or state of a particular discipline.
-
B.
hadInfluenceOn
chosen
Indicates that one entity affected, shaped, or contributed to the development, behavior, or characteristics of another entity.
-
C.
hasTheory
Indicates that an entity possesses, is associated with, or is characterized by a particular theory.
-
D.
hasEnduringInfluenceOn
Indicates that one entity exerts a lasting, long-term impact on another entity’s state, development, or behavior.
-
E.
wereInfluencedBy
Indicates that one entity’s ideas, actions, or characteristics were shaped or affected by 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_69f34980fabc81909819228729a9ca84 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69fff59a00a881909b35b799654b3c45 |
completed | May 10, 2026, 3:03 a.m. |
| PD | Predicate disambiguation | batch_69fff4d0a2e081909c972189b33d0128 |
completed | May 10, 2026, 3 a.m. |
Created at: May 1, 2026, 1:41 a.m.