Triple
T13388638
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CA-5 |
E319509
|
entity |
| Predicate | terminus |
P388
|
FINISHED |
| Object | Tegucigalpa |
E23093
|
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: Tegucigalpa | Statement: [CA-5, terminus, Tegucigalpa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tegucigalpa Context triple: [CA-5, terminus, Tegucigalpa]
-
A.
Tegucigalpa
chosen
Tegucigalpa is the capital and largest city of Honduras, serving as its political, cultural, and economic center.
-
B.
San Pedro Sula
San Pedro Sula is a large industrial and commercial city in northern Honduras, historically known as the country’s economic hub.
-
C.
San José de Comayagua
San José de Comayagua is a municipality and town located in the central Honduran highlands within the Comayagua Department.
-
D.
San Salvador
San Salvador is the largest city of El Salvador and its political, cultural, and economic center.
-
E.
Santiago de los Caballeros
Santiago de los Caballeros is the second-largest city in the Dominican Republic, known as a major cultural, economic, and historical center in the Cibao region.
- 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_69dba0d3a40081909ba49556130ad0e7 |
completed | April 12, 2026, 1:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7461814dc8190aaaefc648da75246 |
completed | May 3, 2026, 12:56 p.m. |
Created at: April 9, 2026, 9:34 p.m.