Triple
T16559389
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Moro people |
E402295
|
entity |
| Predicate | majorLanguage |
P207
|
FINISHED |
| Object | Chavacano language |
E100450
|
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: Chavacano language | Statement: [Moro people, majorLanguage, Chavacano language]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chavacano language Context triple: [Moro people, majorLanguage, Chavacano language]
-
A.
Chavacano
chosen
Chavacano is a Spanish-based creole language spoken in parts of the Philippines, particularly in Zamboanga City and other areas of Mindanao.
-
B.
Chabacano
Chabacano is a major Mexico City Metro transfer station that connects multiple lines and serves as an important transit hub in the city’s network.
-
C.
Serrano language
The Serrano language is an endangered Uto-Aztecan Native American language traditionally spoken by the Serrano people of Southern California.
-
D.
Llanito
Llanito is a unique vernacular spoken in Gibraltar that blends Andalusian Spanish, British English, and elements from other Mediterranean languages.
-
E.
Kaqchikel
Kaqchikel is a Mayan language spoken primarily by the Kaqchikel people in the central highlands of Guatemala.
- 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_69d8838648088190acf97ef11fc3f61b |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3576cceb881908579b56d91b15dec |
completed | April 18, 2026, 10:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0067be809c81909c93eb1253fbf8e5 |
completed | May 10, 2026, 11:10 a.m. |
Created at: April 10, 2026, 5:15 a.m.