Triple
T11036063
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lovelight |
E260887
|
entity |
| Predicate | hasBsideRelationTo |
P15273
|
FINISHED |
| Object | Chiquitita |
E48826
|
NE 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: Chiquitita | Statement: [Lovelight, hasBsideRelationTo, Chiquitita]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chiquitita Context triple: [Lovelight, hasBsideRelationTo, Chiquitita]
-
A.
Chiquitita
chosen
"Chiquitita" is a popular 1979 pop ballad by Swedish group ABBA, known for its uplifting melody and comforting lyrics about offering support in times of sadness.
-
B.
Niño
Niño is a Spanish surname commonly borne by individuals and families in Spanish-speaking countries.
-
C.
Cô Bé
Cô Bé is a youthful female spirit in the Vietnamese Đạo Mẫu tradition, often revered as a playful yet protective attendant of higher mother goddesses.
-
D.
Miss Chiquita
Miss Chiquita is the iconic cartoon spokeswoman used in Chiquita’s advertising and packaging, typically depicted as a stylized banana with a fruit-laden hat.
-
E.
Niña
Niña was one of the three ships in Christopher Columbus’s 1492 voyage across the Atlantic, notable for its role in the first European expedition to the Americas.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d6aa979bdc8190bf0e79104cc098c1 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d797e9e3fc8190802195ac9fcb8e28 |
completed | April 9, 2026, 12:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3e73e7f68819097213e4601c07ee8 |
completed | April 18, 2026, 8:19 p.m. |
Created at: April 8, 2026, 9:25 p.m.