Triple
T6266907
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pan |
E140435
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object | Mack |
E306340
|
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: Mack | Statement: [Pan, hasCharacter, Mack]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mack Context triple: [Pan, hasCharacter, Mack]
-
A.
Mack
chosen
Mack is a given name commonly used as a masculine first name or nickname in English-speaking countries.
-
B.
Mack Maine
Mack Maine is an American rapper and record executive best known as the president of Young Money Entertainment and a close collaborator of Lil Wayne.
-
C.
Mack Trucks
Mack Trucks is a historic American truck manufacturing company best known for its heavy-duty commercial vehicles and iconic bulldog logo.
-
D.
Niva
Niva was a prominent Russian literary and illustrated weekly magazine of the late 19th and early 20th centuries, known for publishing fiction, poetry, and cultural commentary.
-
E.
Hummer
Hummer is a line of large, military-inspired sport utility vehicles known for their rugged off-road capability, boxy design, and high fuel consumption.
- 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_69c008cabc4081909723e2547c9d6cc0 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0639fdad081908492c44d369df8c5 |
completed | March 22, 2026, 9:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c51939cc6081909e491bd16fab595b |
completed | March 26, 2026, 11:32 a.m. |
Created at: March 22, 2026, 4:25 p.m.