Triple
T12027425
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Telcel Theatre |
E286313
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object | Telcel |
E913457
|
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: Telcel | Statement: [Telcel Theatre, namedAfter, Telcel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Telcel Context triple: [Telcel Theatre, namedAfter, Telcel]
-
A.
América Móvil
chosen
América Móvil is a Mexican telecommunications giant and one of the largest mobile network operators in Latin America, controlled by billionaire Carlos Slim.
-
B.
Telmex
Telmex is a major Mexican telecommunications company that provides telephone, internet, and related services across Mexico and parts of Latin America.
-
C.
Tigo
Tigo is a multinational telecommunications company that provides mobile, internet, and digital services across several countries in Latin America and Africa.
-
D.
Celtel International
Celtel International was a pioneering mobile telecommunications company that rapidly expanded cellular services across multiple African countries in the late 1990s and early 2000s.
-
E.
Telefónica S.A.
Telefónica S.A. is a major Spanish multinational telecommunications company that provides mobile, fixed-line, and broadband services across Europe and Latin America.
- 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_69d6ab4669e48190b59246358b0383ab |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d903f13ae8819097a5740f7c51df82 |
completed | April 10, 2026, 2:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f48b8111b88190a42a8904a2d26862 |
completed | May 1, 2026, 11:16 a.m. |
Created at: April 8, 2026, 9:47 p.m.