Triple
T4645216
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wolff |
E101755
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Wolf |
E154589
|
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: Wolf | Statement: [Wolff, hasVariant, Wolf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wolf Context triple: [Wolff, hasVariant, Wolf]
-
A.
Wolf
Wolf is a 2013 studio album by American rapper and producer Tyler, the Creator, known for its eclectic production and introspective, narrative-driven lyrics.
-
B.
Wolf
chosen
Wolf is a common German surname borne by numerous notable individuals across fields such as scholarship, politics, and the arts.
-
C.
Wolf
Wolf is a song by American singer Miguel from his album "War & Leisure."
-
D.
Wolf Den
Wolf Den is a notable rocky cave and historic landmark within Mashamoquet State Park in Connecticut, traditionally associated with early colonial wolf-hunting legends.
-
E.
Timber Wolf
Timber Wolf is a classic wooden roller coaster known for its intense drops and rough, fast-paced ride experience at the Worlds of Fun amusement park.
- 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_69bd43d3bc7c81908f81fcf380476b0f |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd623815288190b21cf59a3786363d |
completed | March 20, 2026, 3:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdfadc5dc081908d56a49895105efb |
completed | March 21, 2026, 1:56 a.m. |
Created at: March 20, 2026, 1:14 p.m.