Triple
T13672003
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Waah! (music video) |
E327772
|
entity |
| Predicate | basedOn |
P98
|
FINISHED |
| Object | Waah! |
E327772
|
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: Waah! | Statement: [Waah! (music video), basedOn, Waah!]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Waah! Context triple: [Waah! (music video), basedOn, Waah!]
-
A.
Waah!
chosen
"Waah!" is a popular hit song by Tanzanian artist Diamond Platnumz, known for its catchy Afro-pop sound and widespread success across East Africa and beyond.
-
B.
Wah
Wah is a city in Pakistan’s Punjab province, known for its industrial significance and proximity to major military and educational institutions.
-
C.
Woah
"Woah" is a popular hip-hop single by American rapper Lil Baby known for its catchy hook and viral dance.
-
D.
WOAH
WOAH is the acronym for the World Organisation for Animal Health, an intergovernmental body that sets international standards for animal health and welfare.
-
E.
Woohoo
"Woohoo" is an energetic, genre-blending pop track by Christina Aguilera featuring Nicki Minaj, known for its playful lyrics and bold, futuristic production.
- 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_69d8076f1fa8819094664a59b55010df |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc6599c248190b7f134b5b9947a23 |
completed | April 12, 2026, 4:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f78b1222648190a70f50e6e5c34593 |
completed | May 3, 2026, 5:51 p.m. |
Created at: April 9, 2026, 9:53 p.m.