Triple
T16961025
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LATAM Perú |
E411426
|
entity |
| Predicate | callsign |
P1565
|
FINISHED |
| Object | LANPERU |
E411427
|
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: LANPERU | Statement: [LATAM Perú, callsign, LANPERU]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LANPERU Context triple: [LATAM Perú, callsign, LANPERU]
-
A.
LANPERU
chosen
LANPERU is the radio callsign used by LATAM Perú, a Peruvian subsidiary airline of the LATAM Airlines Group.
-
B.
Lepar
Lepar is an island in Indonesia’s Bangka Belitung Islands province, known for its coastal landscapes and role in the region’s maritime and resource-based activities.
-
C.
Perrepa Perrepa
Perrepa Perrepa is an Indigenous people known by the ethnonym "Perrepa Perrepa," representing their distinct cultural and ethnic identity.
-
D.
Japeri
Japeri is a municipality in the state of Rio de Janeiro, Brazil, known for its location in the Baixada Fluminense region and its role as a railway hub.
-
E.
Addaperle
Addaperle is a comedic good witch character in "The Wiz" universe, known for her quirky, bumbling magic and warm-hearted guidance.
- 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_69d886c9c9d481909afe222093641cae |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d0209a9081909d9c62456bc16e14 |
completed | April 18, 2026, 6:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00d46ad58c8190be9f0b36daba8162 |
completed | May 10, 2026, 6:54 p.m. |
Created at: April 10, 2026, 5:31 a.m.