Triple
T3536644
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lord Business |
E74787
|
entity |
| Predicate | weapon |
P6948
|
FINISHED |
| Object | Kragle |
E367199
|
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: Kragle | Statement: [Lord Business, weapon, Kragle]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kragle Context triple: [Lord Business, weapon, Kragle]
-
A.
Kragle
chosen
The Kragle is a powerful superweapon made from a tube of Krazy Glue that the villain Lord Business uses to freeze the LEGO world in place in *The LEGO Movie*.
-
B.
Krakolye
Krakolye is a historic village in northwestern Russia known as one of the traditional settlement areas of the Votic people and their endangered Uralic language.
-
C.
Kruklanki
Kruklanki is a village in northern Poland known for its scenic lakes and forests within the Warmian-Masurian region.
-
D.
Grocka
Grocka is a suburban municipality of Belgrade in Serbia, known for its agricultural production, especially fruit growing, and its location along the Danube River.
-
E.
Klecko
Klecko is the surname of former American football defensive lineman Joe Klecko, best known for his standout career with the New York Jets as part of the “New York Sack Exchange.”
- 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_69ad85d1a3948190931fd1ea1f49717b |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbcc7b92481908d2d99948780f4d0 |
completed | March 8, 2026, 6:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3bb87c3748190bce62e86fcdfa380 |
completed | March 13, 2026, 7:23 a.m. |
Created at: March 8, 2026, 3:20 p.m.