Triple
T19988713
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kissin’ You |
E494001
|
entity |
| Predicate | performer |
P1363
|
FINISHED |
| Object | Total |
—
|
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: Total | Statement: [Kissin’ You, performer, Total]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Total Context triple: [Kissin’ You, performer, Total]
-
A.
Total
chosen
Total is an American R&B girl group best known for their 1990s hits and frequent collaborations with Bad Boy Records artists.
-
B.
Total
Total is the former name of TotalEnergies, a major French multinational oil and gas company that has expanded into broader energy sectors.
-
C.
Tutto
Tutto is a major conceptual artwork by Italian artist Alighiero Boetti, consisting of densely packed, brightly colored embroidered shapes that collectively form an all-encompassing visual mosaic.
-
D.
Tout
Tout is an alternative transliteration of Thout, the first month of the ancient Egyptian and Coptic calendars.
-
E.
Tout
"Tout" is a popular French-language ballad by Belgian-Canadian singer Lara Fabian that helped establish her international success in the late 1990s.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da626a67648190af9653832a3aeced |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e65fddfee081909aa8cd6e279b2a7a |
completed | April 20, 2026, 5:18 p.m. |
Created at: April 11, 2026, 3:30 p.m.