Triple
T4435132
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Last Train to Paris |
E95629
|
entity |
| Predicate | featuresArtist |
P1952
|
FINISHED |
| Object | Lil Wayne |
E26484
|
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: Lil Wayne | Statement: [Last Train to Paris, featuresArtist, Lil Wayne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lil Wayne Context triple: [Last Train to Paris, featuresArtist, Lil Wayne]
-
A.
Lil Wayne
chosen
Lil Wayne is a Grammy-winning American rapper, singer, and songwriter known for his influential role in hip-hop and prolific output since the late 1990s.
-
B.
Nelly
Nelly is an American rapper, singer, and songwriter known for early-2000s hits like "Hot in Herre" and "Dilemma."
-
C.
Nelly
Nelly is a Canadian singer-songwriter known for her eclectic pop sound and hits like "I'm Like a Bird" and "Promiscuous."
-
D.
Wiz Khalifa
Wiz Khalifa is an American rapper, singer, and songwriter known for hits like "Black and Yellow" and his laid-back, weed-centric persona.
-
E.
T-Pain
T-Pain is an American singer, rapper, and producer known for popularizing the creative use of Auto-Tune in mainstream hip-hop and R&B music.
- 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_69b3453ea2b48190a26f154b3b8fece5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35588e99881908fea7b71a33e2bb6 |
completed | March 13, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bee05440cc8190b0ab8d0c0811899b |
completed | March 21, 2026, 6:15 p.m. |
Created at: March 12, 2026, 11:31 p.m.