Triple
T4807504
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cinnamon Girl |
E106981
|
entity |
| Predicate | hasNotableCoverVersionBy |
P11142
|
FINISHED |
| Object | Luna |
E27944
|
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: Luna | Statement: [Cinnamon Girl, hasNotableCoverVersionBy, Luna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Luna Context triple: [Cinnamon Girl, hasNotableCoverVersionBy, Luna]
-
A.
Luna
Luna was an ancient Roman town in northern Italy that served as a key urban and commercial center for the Ligurian region.
-
B.
Luna
chosen
Luna is the natural satellite of Earth, renowned for its phases, influence on tides, and prominence in human culture and mythology.
-
C.
Selene
Selene is the tourist lunar excursion vehicle featured in Arthur C. Clarke’s science fiction novel "A Fall of Moondust."
-
D.
Selene
Selene is the Greek goddess and personification of the Moon, often depicted driving a silver chariot across the night sky.
-
E.
Luneta
Luneta is the historic urban park in Manila, Philippines, renowned as a national landmark and popular public gathering place.
- 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_69bd43f779448190b92885cb70abb6c2 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6c69099c8190956b6df42bb922c8 |
completed | March 20, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be4da2a1808190bd8d0c073e4bad07 |
completed | March 21, 2026, 7:49 a.m. |
Created at: March 20, 2026, 1:23 p.m.