Triple
T12991372
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Selebobo |
E321912
|
entity |
| Predicate | hasCollaborationWith |
P398
|
FINISHED |
| Object | Tekno Miles |
E298317
|
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: Tekno Miles | Statement: [Selebobo, hasCollaborationWith, Tekno Miles]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tekno Miles Context triple: [Selebobo, hasCollaborationWith, Tekno Miles]
-
A.
Miles&Go
Miles&Go is TAP Air Portugal’s loyalty program that allows members to earn and redeem miles for flights, upgrades, and other travel-related benefits.
-
B.
Tekno
chosen
Tekno is a Nigerian singer, songwriter, and record producer known for his Afrobeat and Afropop hit songs and dance-oriented sound.
-
C.
Mili
Mili is the main settlement and administrative center of Mili Atoll in the Marshall Islands.
-
D.
Mili
Mili is a 1975 Hindi drama film directed by Hrishikesh Mukherjee, known for Jaya Bhaduri’s acclaimed performance as a spirited young woman facing a terminal illness.
-
E.
Qmiles
Qmiles are the frequent-flyer reward points earned and redeemed by members of Qatar Airways’ Privilege Club loyalty program.
- 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_69d8076479b8819090afce3591939cdf |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97e7765788190a9503ef055bc30ca |
completed | April 10, 2026, 10:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6b8fb70f481908a9a4ca04d6bf93b |
completed | May 3, 2026, 2:54 a.m. |
Created at: April 9, 2026, 8:43 p.m.