Triple
T20561216
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Phase Two |
E504846
|
entity |
| Predicate | introducesCharacter |
P12208
|
FINISHED |
| Object | Groot |
—
|
NE NERFINISHED |
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: Groot | Statement: [Phase Two, introducesCharacter, Groot]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Groot Context triple: [Phase Two, introducesCharacter, Groot]
-
A.
Groot
chosen
Groot is a sentient, tree-like alien superhero from Marvel Comics and the Marvel Cinematic Universe, known for his limited vocabulary and close partnership with Rocket Raccoon.
-
B.
Skaar
Skaar is a Marvel Comics character, the powerful, battle-hardened son of the Hulk who inherits both his father’s gamma strength and his mother’s Oldstrong abilities.
-
C.
Baiju
Baiju is an Indian-American entrepreneur best known as the co-founder of the stock trading platform Robinhood.
-
D.
Baiju
Baiju was a 13th-century Mongol general who led Mongol forces in their campaigns into Eastern Europe.
-
E.
Banzi
Banzi is a town in the Basilicata region of southern Italy, known as the modern site near the ancient Lucanian city of Bantia.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4b6587c8190aee63dc7cff244ea |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6a79ec10481909740eb6a08ae2658 |
completed | April 20, 2026, 10:24 p.m. |
Created at: April 16, 2026, 11:39 a.m.