Triple
T11794563
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shulker |
E280471
|
entity |
| Predicate | target |
P860
|
FINISHED |
| Object | Snow Golems |
E937753
|
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: Snow Golems | Statement: [Shulker, target, Snow Golems]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Snow Golems Context triple: [Shulker, target, Snow Golems]
-
A.
Snow Golem
chosen
A Snow Golem is a player-created, snow-throwing utility mob in Minecraft that attacks hostile creatures and leaves a trail of snow behind it.
-
B.
Snow Miser
Snow Miser is a comically villainous, cold-loving winter spirit from the Rankin/Bass Christmas specials, best known for controlling snow and ice and singing about his frosty powers.
-
C.
Snowman
Snowman is the post-apocalyptic survivor and narrator of Margaret Atwood’s dystopian novel "Oryx and Crake," through whose perspective the story’s ruined world and its origins are revealed.
-
D.
Snoge
Snoge is the historic 17th-century Portuguese-Israelite synagogue in Amsterdam, renowned as one of the oldest and best-preserved Sephardic synagogues in Europe.
-
E.
Snowlets
Snowlets are the four snowy owl mascots created to represent the 1998 Winter Olympics in Nagano, Japan.
- 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_69d6ab258b808190b1735835c841e3a4 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8a5a082d08190a42541396a06ed98 |
completed | April 10, 2026, 7:24 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f09115c66c8190b0a3e775bdf575c1 |
completed | April 28, 2026, 10:51 a.m. |
Created at: April 8, 2026, 9:42 p.m.