Triple
T9416492
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wolfgang |
E227033
|
entity |
| Predicate | hasShortForm |
P43
|
FINISHED |
| Object | Wolfe |
E107098
|
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: Wolfe | Statement: [Wolfgang, hasShortForm, Wolfe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wolfe Context triple: [Wolfgang, hasShortForm, Wolfe]
-
A.
Wolfe
chosen
Wolfe is a surname most famously associated with British Army officer James Wolfe, known for his role in the Seven Years' War and the capture of Quebec.
-
B.
Thunderwolf
Thunderwolf is the athletic mascot of Lakehead University, typically depicted as a fierce wolf symbolizing strength, resilience, and northern spirit.
-
C.
Foege
Foege is the surname of William H. Foege, an American epidemiologist renowned for his pivotal role in the global eradication of smallpox.
-
D.
Jocko
Jocko is a fictional character from the film "Red Dog," appearing as part of the story surrounding the legendary Australian kelpie.
-
E.
Wilk
Wilk was the wartime pseudonym of Aleksander Krzyżanowski, a Polish military officer and commander in the Home Army (Armia Krajowa) during World War II.
- 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_69ca84359e7c819091148ba4b670e436 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd68c9917481909f793a2a9efb2a75 |
completed | April 1, 2026, 6:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d107bfd73481908e07d0ee2774bd59 |
completed | April 4, 2026, 12:44 p.m. |
Created at: March 30, 2026, 7:48 p.m.