Triple
T30331353
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Garota de Ipanema |
E771485
|
entity |
| Predicate | hasEnglishLine |
P67696
|
FINISHED |
| Object | Tall and tan and young and lovely, the girl from Ipanema goes walking |
—
|
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: Tall and tan and young and lovely, the girl from Ipanema goes walking | Statement: [Garota de Ipanema, hasEnglishLine, Tall and tan and young and lovely, the girl from Ipanema goes walking]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEnglishLine Context triple: [Garota de Ipanema, hasEnglishLine, Tall and tan and young and lovely, the girl from Ipanema goes walking]
-
A.
hasEnglishEdition
Indicates that one entity has a version or edition of itself that is produced or available in the English language.
-
B.
hasEnglishIncipit
chosen
Indicates that an entity has an opening phrase or initial text (incipit) expressed in English.
-
C.
hasEnglishName
Indicates that an entity is associated with a name expressed in the English language.
-
D.
hasLanguageOn
Indicates that an entity uses or is associated with a particular language in a specific context, medium, or location.
-
E.
hasEnglishComponent
Indicates that something includes or is associated with a component that is in the English language.
- 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_69f2248aba24819095bb86480d55b23b |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f7aa699d68819081ed363931894ab3 |
completed | May 3, 2026, 8:04 p.m. |
| PD | Predicate disambiguation | batch_69f7a8cec6d48190bebfa884b2f938c0 |
completed | May 3, 2026, 7:58 p.m. |
Created at: April 29, 2026, 7:53 p.m.