Triple
T3650486
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tequesta |
E77405
|
entity |
| Predicate | neighboringPeople |
P11274
|
FINISHED |
| Object | Mayaimi |
E376066
|
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: Mayaimi | Statement: [Tequesta, neighboringPeople, Mayaimi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mayaimi Context triple: [Tequesta, neighboringPeople, Mayaimi]
-
A.
Mayaimi
chosen
Mayaimi refers to a Native American people who historically inhabited the region around Lake Okeechobee in present-day Florida.
-
B.
Mazunte
Mazunte is a small, laid-back beach town on Mexico’s Oaxacan coast, known for its sea turtle conservation center, eco-tourism, and scenic Pacific shoreline.
-
C.
Mocorito
Mocorito is a historic town and municipality in the Mexican state of Sinaloa, known for its colonial architecture and cultural traditions.
-
D.
Guamote
Guamote is a rural town and canton in Ecuador known for its indigenous Kichwa culture, traditional markets, and highland Andean landscapes.
-
E.
Tafoya
Tafoya is the surname of Michele Tafoya, a prominent American sportscaster best known for her work as an NFL sideline reporter.
- 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_69ad85def5cc8190863dccf55a18bebb |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc39303bc819090725643e53a96d6 |
completed | March 8, 2026, 6:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4cde0425081909ca15dba0bcc3fc4 |
completed | March 14, 2026, 2:54 a.m. |
Created at: March 8, 2026, 3:24 p.m.