Triple
T20192557
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lylat System |
E493009
|
entity |
| Predicate | hasPlanet |
P7294
|
FINISHED |
| Object | Titania |
—
|
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: Titania | Statement: [Lylat System, hasPlanet, Titania]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Titania Context triple: [Lylat System, hasPlanet, Titania]
-
A.
Titania
chosen
Titania is the largest of Uranus's moons, an icy, heavily cratered satellite discovered by William Herschel in 1787.
-
B.
Titânia
Titânia is a notable literary work by Portuguese surrealist poet and painter Mário Cesariny de Vasconcelos.
-
C.
Oberon
Oberon is a rural town in New South Wales, Australia, known for its cool climate, timber industry, and proximity to Jenolan Caves and the Blue Mountains.
-
D.
Oberon
Oberon is a modular, type-safe systems programming language designed by Niklaus Wirth as a streamlined successor to Pascal and Modula-2, emphasizing simplicity and efficiency.
-
E.
Oberon
Oberon is one of Uranus's largest and outermost major moons, known for its heavily cratered, icy surface and dark, ancient terrain.
- 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_69da6268a034819081cbd9ea5a1c9475 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66ad654648190a27521e6e8ea40c5 |
completed | April 20, 2026, 6:05 p.m. |
Created at: April 11, 2026, 11:37 p.m.