Triple
T21724608
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dayanara Torres |
E536243
|
entity |
| Predicate | gainedInternationalFameIn |
P117814
|
FINISHED |
| Object | 1990s |
—
|
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: 1990s | Statement: [Dayanara Torres, gainedInternationalFameIn, 1990s]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: gainedInternationalFameIn Context triple: [Dayanara Torres, gainedInternationalFameIn, 1990s]
-
A.
fameFor
Indicates that one entity is widely known or recognized specifically because of, or in connection with, another entity.
-
B.
gainedProminenceAs
Indicates that an entity became widely recognized or notable in the role, capacity, or identity specified by another entity.
-
C.
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.
-
D.
helpedPropelToMainstreamFame
Indicates that one entity significantly contributed to another entity’s rise to widespread public recognition or mainstream popularity.
-
E.
famousAt
chosen
Indicates that an entity is widely known or recognized in a particular place, context, or time.
- 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_69e0c46d3284819099a4f9d5a704eb95 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69efd9722ce881909372fd0f1b15d242 |
completed | April 27, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69e6969725bc81908e7ad19619ba2688 |
completed | April 20, 2026, 9:11 p.m. |
Created at: April 16, 2026, 6:48 p.m.