Triple
T12139432
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Selina |
E289144
|
entity |
| Predicate | isAssociatedWith |
P2830
|
FINISHED |
| Object | moon goddess Selene |
E111954
|
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: moon goddess Selene | Statement: [Selina, isAssociatedWith, moon goddess Selene]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: moon goddess Selene Context triple: [Selina, isAssociatedWith, moon goddess Selene]
-
A.
Selene
Selene is the tourist lunar excursion vehicle featured in Arthur C. Clarke’s science fiction novel "A Fall of Moondust."
-
B.
Selene
chosen
Selene is the Greek goddess and personification of the Moon, often depicted driving a silver chariot across the night sky.
-
C.
Luna
Luna is the live wolf mascot that represents the University of Nevada's Wolf Pack football program.
-
D.
Luna
Luna is a magical flying jaquin who serves as one of Princess Elena’s loyal animal companions in the animated series "Elena of Avalor."
-
E.
Luna
Luna is the gentle, talking moon character who serves as a wise, comforting friend and advisor to Bear in the children's television series "Bear in the Big Blue House."
- 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_69d6ab4b5e4c81909950b17151eb0951 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9158eef48819083bdce283a363414 |
completed | April 10, 2026, 3:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f62a81f6708190a6a215421f8ce8dd |
completed | May 2, 2026, 4:46 p.m. |
Created at: April 8, 2026, 9:49 p.m.