Triple
T20697196
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Giant-Size X-Men #1 |
E508687
|
entity |
| Predicate | featuresCharacter |
P626
|
FINISHED |
| Object | Beast |
—
|
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: Beast | Statement: [Giant-Size X-Men #1, featuresCharacter, Beast]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Beast Context triple: [Giant-Size X-Men #1, featuresCharacter, Beast]
-
A.
Beast
Beast is a major character in the comic series and game "The Wolf Among Us," depicted as a reformed fairy-tale monster struggling to maintain his human façade and relationship with his wife, Beauty, in the gritty Fabletown community.
-
B.
Beast
Beast is a film project on which Megan Gill contributed her professional editing expertise.
-
C.
Beast
Beast is a thriller novel by Peter Benchley that centers on a deadly giant squid terrorizing a coastal community.
-
D.
Beast
chosen
Beast is a brilliant mutant scientist and acrobatic fighter known for his blue-furred, beast-like appearance and long-standing membership in the X-Men and Avengers.
-
E.
Beast
Beast is the short name of the Brampton Beast, a former professional ice hockey team based in Brampton, Ontario, that competed in the ECHL.
- 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_69e0b4c2b2a481909e31e9cb8f81ab55 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6c1123d7c81908a1d16923437266d |
completed | April 21, 2026, 12:13 a.m. |
Created at: April 16, 2026, 12:11 p.m.