Triple
T11983212
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jasmine |
E285209
|
entity |
| Predicate | pet |
P8711
|
FINISHED |
| Object | Rajah |
E877150
|
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: Rajah | Statement: [Jasmine, pet, Rajah]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rajah Context triple: [Jasmine, pet, Rajah]
-
A.
Rajah
chosen
Rajah is the loyal and protective tiger companion of Princess Jasmine in Disney's Aladdin franchise.
-
B.
Raja
Raja is a traditional Indian royal title historically used by Hindu monarchs and regional rulers.
-
C.
Tuan Besar
Tuan Besar was a Malay honorific title denoting the ruling White Rajah of Sarawak, signifying his status as the paramount leader.
-
D.
Tuanku
Tuanku is a Malay royal honorific style traditionally used for reigning monarchs and high-ranking nobility in Malaysia.
-
E.
The Rajah
The Rajah is the nickname of Roger Brown, a prominent American art historian and curator known for his influential work in the field of art history.
- 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_69d6ab44a77c8190a652f4b27164e4ef |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d903973c848190aac871d6dfecc74b |
completed | April 10, 2026, 2:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f472286edc8190ac72d7dd2b646c91 |
completed | May 1, 2026, 9:28 a.m. |
Created at: April 8, 2026, 9:46 p.m.