Triple
T22166554
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Latin trap |
E547801
|
entity |
| Predicate | hasNotableArtist |
P2487
|
FINISHED |
| Object | Arcángel |
—
|
NE NERFINISHED |
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: Arcángel | Statement: [Latin trap, hasNotableArtist, Arcángel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Arcángel Context triple: [Latin trap, hasNotableArtist, Arcángel]
-
A.
Arcángel
chosen
Arcángel is a Puerto Rican-American reggaeton and Latin trap singer and songwriter known for his influential role in the urban Latin music scene.
-
B.
Ángel
Ángel is a given name of Spanish origin commonly used for males and derived from the word for “angel.”
-
C.
Artangel
Artangel is a London-based arts organization known for producing ambitious, site-specific and often socially engaged contemporary art projects.
-
D.
Isangel
Isangel is a small coastal town on Tanna Island in Vanuatu that serves as an administrative center and gateway for visitors to the active volcano Mount Yasur.
-
E.
Anděl
Anděl is a major Prague Metro station and busy transport hub located in the Smíchov district of the city.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e3c4c5c81908d336165816b12e0 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12a3213ec8190841439dbe470d545 |
completed | April 28, 2026, 9:44 p.m. |
Created at: April 16, 2026, 8:34 p.m.