Triple
T15412853
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boo |
E368635
|
entity |
| Predicate | allyOf |
P4662
|
FINISHED |
| Object | King Boo |
E368635
|
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: King Boo | Statement: [Boo, allyOf, King Boo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: King Boo Context triple: [Boo, allyOf, King Boo]
-
A.
Boo
Boo is a statically typed, Python-inspired programming language for the .NET platform that was once used as a primary scripting option in the Unity game engine.
-
B.
Boo
Boo is the reclusive, mysterious neighbor in Harper Lee’s novel "To Kill a Mockingbird," whose true kindness is revealed over the course of the story.
-
C.
Boo
Boo is a suburban district and island area in the Stockholm archipelago, located within Nacka Municipality in Sweden.
-
D.
Boo
Boo is the young human girl in Pixar's animated film "Monsters, Inc." whose unexpected arrival in the monster world drives the story's central conflict and emotional core.
-
E.
Boo
chosen
Boo is a recurring ghost-like enemy in the Super Mario series, known for covering its face when looked at and attacking when the player’s back is turned.
- 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_69d85a16c68c819099c1b547fbc87b32 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03ea600b48190a3dbca1a68a2a1cd |
completed | April 16, 2026, 1:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff1a754d1881909ed322479bab460d |
completed | May 9, 2026, 11:28 a.m. |
Created at: April 10, 2026, 3:20 a.m.