Triple
T6378974
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thin Lizzy |
E143534
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Snowy White |
E528525
|
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: Snowy White | Statement: [Thin Lizzy, hasPart, Snowy White]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Snowy White Context triple: [Thin Lizzy, hasPart, Snowy White]
-
A.
Snowy White
chosen
Snowy White is a British blues and rock guitarist known for his work with Thin Lizzy, Pink Floyd-related projects, and his own solo career.
-
B.
Blancanieves
Blancanieves is a 2012 Spanish silent black-and-white fantasy drama film that reimagines the Snow White fairy tale in 1920s Spain.
-
C.
the Witch from Rapunzel
The Witch from Rapunzel is the powerful, overprotective sorceress who imprisons Rapunzel in a tower and serves as the primary antagonist in the classic fairy tale.
-
D.
The Evil Queen
The Evil Queen is the vain and power-hungry royal villain from Disney’s Snow White, infamous for her jealousy and use of dark magic to eliminate her rival.
-
E.
Elsa
Elsa is a feminine given name of Germanic origin, widely recognized today through its use for the main character in Disney's animated film "Frozen."
- 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_69c008d9f4348190ab598a2913259a1c |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0685029488190911fb24c470b6f0d |
completed | March 22, 2026, 10:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c62da95aac81909e5e6d310168a5f6 |
completed | March 27, 2026, 7:11 a.m. |
Created at: March 22, 2026, 4:33 p.m.