Triple
T33080488
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Matthew effect |
E846491
|
entity |
| Predicate | popularizedInWork |
P71099
|
FINISHED |
| Object | "The Matthew Effect in Science" |
—
|
NE NERFINISHED |
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: "The Matthew Effect in Science" | Statement: [Matthew effect, popularizedInWork, "The Matthew Effect in Science"]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: popularizedInWork Context triple: [Matthew effect, popularizedInWork, "The Matthew Effect in Science"]
-
A.
popularizedByWork
chosen
Indicates that something became widely known, accepted, or influential as a result of a particular work (such as a book, film, or artwork).
-
B.
popularizedIn
Indicates that something became widely known, accepted, or fashionable within a particular place, time period, or context.
-
C.
popularizedBy
Indicates that something became widely known, accepted, or fashionable as a result of the influence or actions of a particular agent.
-
D.
popularizedInEnglishBy
Indicates that one entity is responsible for making another entity widely known or commonly used within the English language context.
-
E.
popularizedAfter
Indicates that one entity became widely known, accepted, or influential only after another specified entity had already gained popularity.
- 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_69f34954d46c8190a04a159cc5f99efd |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69fbc9d1dba881908c399b8e1dc13ce2 |
completed | May 6, 2026, 11:08 p.m. |
| PD | Predicate disambiguation | batch_69fbc8ec03ac8190a757563f96fab283 |
completed | May 6, 2026, 11:04 p.m. |
Created at: May 1, 2026, 1:26 a.m.