Triple
T30762424
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Do Wah Diddy Diddy |
E783268
|
entity |
| Predicate | madeInternationallyFamousBy |
P170102
|
FINISHED |
| Object | Manfred Mann |
—
|
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: Manfred Mann | Statement: [Do Wah Diddy Diddy, madeInternationallyFamousBy, Manfred Mann]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: madeInternationallyFamousBy Context triple: [Do Wah Diddy Diddy, madeInternationallyFamousBy, Manfred Mann]
-
A.
fameFor
Indicates that one entity is widely known or recognized specifically because of, or in connection with, another entity.
-
B.
starMadeFamous
Indicates that one entity (such as a work, event, or role) is what caused another entity (typically a person) to become widely known or famous.
-
C.
organFamousFor
Indicates that an organ is widely recognized or notable for a particular characteristic, function, or association.
-
D.
madeFamousInYear
Indicates that an entity became widely known or gained significant fame in a specified year.
-
E.
madeFamousByFilm
Indicates that something became widely known or gained significant public recognition as a result of being featured in a film.
- F. None of above. chosen
Provenance (4 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_69f224b047f48190b4f5efeb7ee97b37 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f68fba02708190a1d0392453790fd8 |
completed | May 2, 2026, 11:58 p.m. |
| PD | Predicate disambiguation | batch_69f686140aa08190a35f62572b2db9b6 |
completed | May 2, 2026, 11:17 p.m. |
| PDg | Predicate description generation | batch_69f68848ad348190a2fb6e841dcfdb7d |
completed | May 2, 2026, 11:27 p.m. |
Created at: April 29, 2026, 8:39 p.m.