Triple
T6597586
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dua Lipa |
E148514
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Lipa |
E430659
|
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: Lipa | Statement: [Dua Lipa, familyName, Lipa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lipa Context triple: [Dua Lipa, familyName, Lipa]
-
A.
Lipa
chosen
Lipa is a highly urbanized city in the province of Batangas in the Calabarzon region of the Philippines, known as a commercial, educational, and religious center.
-
B.
Mwinilunga
Mwinilunga is a town in northwestern Zambia known as an administrative and commercial center near the borders with Angola and the Democratic Republic of the Congo.
-
C.
Kilembe
Kilembe is a town in western Uganda that serves as a common starting point for treks to the Rwenzori Mountains, including ascents of Margherita Peak.
-
D.
Kibondo
Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
-
E.
Mutombo
Mutombo is a retired Congolese-American NBA Hall of Fame center renowned for his dominant shot-blocking, defensive prowess, and humanitarian work.
- 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_69c687e7b8688190811ffee72e096468 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6aeee738081908913f4f8c6699bd9 |
completed | March 27, 2026, 4:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6cbc3ee188190a285110707a895b6 |
completed | March 27, 2026, 6:26 p.m. |
Created at: March 27, 2026, 1:56 p.m.