Triple
T16260834
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wolf Racing |
E394749
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object |
Wolf
Wolf is a Canadian motorsport team and former Formula One constructor known for competing in the late 1970s under the name Walter Wolf Racing.
|
E1201729
|
NE FINISHED |
How this triple was built (4 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: [Wolf Racing, shortName, Wolf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wolf Context triple: [Wolf Racing, shortName, Wolf]
-
A.
Wolf
Wolf is a common German surname borne by numerous notable individuals across fields such as scholarship, politics, and the arts.
-
B.
Wolf
Wolf is a song by American singer Miguel from his album "War & Leisure."
-
C.
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.
-
D.
Wolf
The Wolf is a powerful, social canine predator known for living and hunting in packs across the Northern Hemisphere.
-
E.
Wolf
Wolf is a central character in August Wilson’s play "Two Trains Running," known as a fast-talking numbers runner who operates out of Memphis’s diner in 1960s Pittsburgh.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Wolf Triple: [Wolf Racing, shortName, Wolf]
Generated description
Wolf is a Canadian motorsport team and former Formula One constructor known for competing in the late 1970s under the name Walter Wolf Racing.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Wolf Target entity description: Wolf is a Canadian motorsport team and former Formula One constructor known for competing in the late 1970s under the name Walter Wolf Racing.
-
A.
Wolf
The Wolf is a powerful, social canine predator known for living and hunting in packs across the Northern Hemisphere.
-
B.
Wolf
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 1994 American horror-romance film starring Jack Nicholson and Michelle Pfeiffer, blending werewolf mythology with corporate drama.
-
D.
Wolf
Wolf is a central character in August Wilson’s play "Two Trains Running," known as a fast-talking numbers runner who operates out of Memphis’s diner in 1960s Pittsburgh.
-
E.
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.
- F. None of above. chosen
Provenance (5 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_69d87f221d8081909b0b2063e7528ba2 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e245c3e5388190942b0237ab5d1f0f |
completed | April 17, 2026, 2:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a000eee110c819088d99b80435ab70b |
completed | May 10, 2026, 4:51 a.m. |
| NEDg | Description generation | batch_6a000f7e6338819099598bc22d31cd22 |
completed | May 10, 2026, 4:54 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a001025300c819084933d9c6d19fe97 |
completed | May 10, 2026, 4:57 a.m. |
Created at: April 10, 2026, 5:04 a.m.