Triple
T82467
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | El Salvador |
E1657
|
entity |
| Predicate | largestCity |
P235
|
FINISHED |
| Object | San Salvador |
E15340
|
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: San Salvador | Statement: [El Salvador, largestCity, San Salvador]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: San Salvador Context triple: [El Salvador, largestCity, San Salvador]
-
A.
San Salvador
chosen
San Salvador is the largest city of El Salvador and its political, cultural, and economic center.
-
B.
El Salvador
El Salvador is a Central American country known for being the smallest and most densely populated nation in the region, with a history of civil conflict and a recent push toward economic modernization and cryptocurrency adoption.
-
C.
Santiago de Veraguas
Santiago de Veraguas is a principal urban and commercial center in western Panama and the capital of Veraguas Province.
-
D.
Guayaquil
Guayaquil is a major Pacific port city in southwestern Ecuador and the country’s principal commercial and industrial center.
-
E.
Honduras
Honduras is a Central American country known for its mountainous terrain, Caribbean and Pacific coastlines, and rich Mayan and colonial heritage.
- 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_69a24c8150408190910a693eb51c1f71 |
completed | Feb. 28, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69a24f367b208190a69f5b76d6ae0496 |
completed | Feb. 28, 2026, 2:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a2b014ab2c8190bcef8382280932dc |
completed | Feb. 28, 2026, 9:06 a.m. |
Created at: Feb. 28, 2026, 2:06 a.m.