Triple
T1345862
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Friedrich August Wolf |
E28569
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Wolf
Wolf is a common German surname borne by numerous notable individuals across fields such as scholarship, politics, and the arts.
|
E154589
|
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: [Friedrich August Wolf, familyName, Wolf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wolf Context triple: [Friedrich August Wolf, familyName, Wolf]
-
A.
Wolves
"Wolves" is a moody, atmospheric hip-hop track by Kanye West that blends haunting production with introspective lyrics and prominent guest vocals.
-
B.
Wolverines
Wolverines are the fierce, wolverine-themed mascots representing the University of Michigan’s athletic teams, especially its storied football program.
-
C.
Panther
The Panther is the fierce and agile feline mascot representing Clark Atlanta University’s athletic teams and school spirit.
-
D.
Lynx
Lynx is a high-speed serial computer bus interface standard, better known as IEEE 1394 or FireWire, used for real-time data transfer between digital devices.
-
E.
Lion
Lion is a 2016 biographical drama film about an Indian boy separated from his family and adopted in Australia who later uses Google Earth to find his way home.
- 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: [Friedrich August Wolf, familyName, Wolf]
Generated description
Wolf is a common German surname borne by numerous notable individuals across fields such as scholarship, politics, and the arts.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Wolf Target entity description: Wolf is a common German surname borne by numerous notable individuals across fields such as scholarship, politics, and the arts.
-
A.
Wolves
"Wolves" is a moody, atmospheric hip-hop track by Kanye West that blends haunting production with introspective lyrics and prominent guest vocals.
-
B.
Wolverines
Wolverines are the fierce, wolverine-themed mascots representing the University of Michigan’s athletic teams, especially its storied football program.
-
C.
Panther
The Panther is the fierce and agile feline mascot representing Clark Atlanta University’s athletic teams and school spirit.
-
D.
Lynx
Lynx is a high-speed serial computer bus interface standard, better known as IEEE 1394 or FireWire, used for real-time data transfer between digital devices.
-
E.
Lion
Lion is a 2016 biographical drama film about an Indian boy separated from his family and adopted in Australia who later uses Google Earth to find his way home.
- 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_69a49854eb3481908c7d56b2e449a290 |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c23e84188190b0395c57dd45b62a |
completed | March 1, 2026, 10:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acc6351cbc81909e2ffc692ee92b54 |
completed | March 8, 2026, 12:43 a.m. |
| NEDg | Description generation | batch_69acc6af0db88190a02936072783553e |
completed | March 8, 2026, 12:45 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69acc7246f94819095b6a6868e06a7bf |
completed | March 8, 2026, 12:47 a.m. |
Created at: March 1, 2026, 7:56 p.m.