Triple
T76206
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ET |
E1522
|
entity |
| Predicate | usedInCity |
P4810
|
FINISHED |
| Object | Lima |
E2605
|
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: Lima | Statement: [ET, usedInCity, Lima]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lima Context triple: [ET, usedInCity, Lima]
-
A.
Lima
chosen
Lima is the capital and largest city of Peru, known as a major political, economic, and cultural center on South America's Pacific coast.
-
B.
Guayaquil
Guayaquil is a major Pacific port city in southwestern Ecuador and the country’s principal commercial and industrial center.
-
C.
Santiago
Santiago is the capital and primary economic, political, and cultural center of Chile, located in the country’s central valley.
-
D.
Quito
Quito is the high-altitude Andean city that serves as Ecuador’s political and cultural center, renowned for its well-preserved colonial historic center and dramatic mountain setting.
-
E.
Buenos Aires
Buenos Aires is the capital and largest city of Argentina, known for its rich European-influenced culture, tango music and dance, and vibrant urban life.
- 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_69a24c60d19c8190a1b6c105ca59ef5b |
completed | Feb. 28, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69a2567c90308190a9b989c586f7e559 |
completed | Feb. 28, 2026, 2:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a284fb8c1481908d7796593836c925 |
completed | Feb. 28, 2026, 6:02 a.m. |
Created at: Feb. 28, 2026, 2:06 a.m.