Triple
T14190883
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Purple Haze 2 |
E351708
|
entity |
| Predicate | hasContributor |
P4244
|
FINISHED |
| Object | Max B |
E536507
|
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: Max B | Statement: [Purple Haze 2, hasContributor, Max B]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Max B Context triple: [Purple Haze 2, hasContributor, Max B]
-
A.
Max B
chosen
Max B is an American rapper and singer from Harlem known for his melodic, wavy style and significant influence on New York hip-hop.
-
B.
Max-D
Max-D is a famous Monster Jam freestyle and racing monster truck known for its spiked body, aggressive design, and high-flying stunts.
-
C.
Michael Max
Michael Max is a fictional American boxer and playable fighter from SNK’s Fatal Fury video game series.
-
D.
Máximo
Máximo is a masculine given name of Spanish origin, historically associated with notable figures such as Cuban independence leader Máximo Gómez.
-
E.
Max
Max is the central protagonist of the French romantic drama film "L'Appartement," around whom the story’s intricate web of love, obsession, and mistaken identity revolves.
- 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_69d827894ac0819097803e57f3227b23 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61df628c8190ba3f557e2128dce5 |
completed | April 14, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd1946eb68819096adf3c16a39818d |
completed | May 7, 2026, 10:59 p.m. |
Created at: April 10, 2026, 1:04 a.m.