Triple
T13389546
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MacKenney cone |
E319538
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Guatemala City |
E16269
|
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: Guatemala City | Statement: [MacKenney cone, near, Guatemala City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Guatemala City Context triple: [MacKenney cone, near, Guatemala City]
-
A.
Guatemala City
chosen
Guatemala City is the capital and largest city of Guatemala, serving as the country’s political, economic, and cultural center.
-
B.
Siguatepeque
Siguatepeque is a Honduran city known as an important commercial and agricultural center in the country’s central highlands.
-
C.
Santiago Atitlán
Santiago Atitlán is a traditional Tz'utujil Maya town in Guatemala known for its vibrant indigenous culture, crafts, and scenic location on the shores of Lake Atitlán.
-
D.
San Pedro Sula
San Pedro Sula is a large industrial and commercial city in northern Honduras, historically known as the country’s economic hub.
-
E.
San Salvador
San Salvador is the largest city of El Salvador and its political, cultural, and economic center.
- 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_69d806b886bc8190b676e7768b8e01c5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dba0d543348190a9c1be509b015c0f |
completed | April 12, 2026, 1:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f75d84d19081909f564c90b50fda60 |
completed | May 3, 2026, 2:36 p.m. |
Created at: April 9, 2026, 9:34 p.m.