Triple
T22599408
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nagada Sang Dhol |
E574770
|
entity |
| Predicate | associatedWithCharacter |
P1481
|
FINISHED |
| Object | Leela |
—
|
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: Leela | Statement: [Nagada Sang Dhol, associatedWithCharacter, Leela]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leela Context triple: [Nagada Sang Dhol, associatedWithCharacter, Leela]
-
A.
Leela
chosen
Leela is a companion of the Fourth Doctor in the classic British science fiction television series Doctor Who.
-
B.
Leela
Leela is a 2002 Indian drama film directed by Somnath Sen, featuring Deepti Naval in a critically acclaimed lead role that explores complex emotional and cultural themes.
-
C.
Leela
Leela is the one-eyed, tough yet compassionate spaceship captain from the animated television series "Futurama."
-
D.
Seeta
Seeta is a rapidly growing suburban town and trading center in central Uganda, located along the Kampala–Jinja highway near Mukono.
-
E.
Neela
Neela is a central street racer and love interest in the film "The Fast and the Furious: Tokyo Drift," known for her drifting skills in Tokyo's underground racing scene.
- 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_69e245bc11308190b69d794d5d1e0bb6 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1626b5f5481909a104088d1c96720 |
completed | April 29, 2026, 1:44 a.m. |
Created at: April 17, 2026, 2:50 p.m.