Triple
T2204893
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Can You Feel the Love Tonight |
E50573
|
entity |
| Predicate | associatedFilmCharacter |
P12208
|
FINISHED |
| Object | Nala |
E173777
|
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: Nala | Statement: [Can You Feel the Love Tonight, associatedFilmCharacter, Nala]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nala Context triple: [Can You Feel the Love Tonight, associatedFilmCharacter, Nala]
-
A.
Nala
chosen
Nala is a courageous lioness from Disney's "The Lion King," known as Simba's childhood friend and later queen of the Pride Lands.
-
B.
Zibelle
Zibelle is a village in eastern Germany, historically part of Lusatia, known in this context as the place where physicist Walther Nernst died.
-
C.
Nene
Nene was the principal wife of Japanese warlord Toyotomi Hideyoshi and a politically influential noblewoman during the late Sengoku period.
-
D.
Luna the Wolf
Luna the Wolf is the costumed wolf mascot representing the athletic teams and school spirit of the University of Nevada, Reno.
-
E.
Misti
Misti is a prominent, snow-capped stratovolcano overlooking the city of Arequipa in southern Peru.
- 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_69a88b044ab48190add007487680f009 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc5b101d48190a321625720d537b6 |
completed | March 7, 2026, 6:29 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae654976908190bad6352be1e33d61 |
completed | March 9, 2026, 6:14 a.m. |
Created at: March 4, 2026, 7:46 p.m.