Triple
T8181539
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Martín Line |
E191072
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object | Caseros |
E438559
|
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: Caseros | Statement: [San Martín Line, hasStation, Caseros]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Caseros Context triple: [San Martín Line, hasStation, Caseros]
-
A.
Barracas
Barracas is a traditional working-class neighborhood in Buenos Aires, Argentina, known for its historic architecture, industrial past, and strong local identity.
-
B.
Junín
Junín is a central highland region of Peru known for its Andean landscapes, rich mining and agricultural activities, and historical role in Peru’s independence.
-
C.
Junín
Junín is a major oil-producing area within Venezuela’s Orinoco Belt, known for its vast extra-heavy crude reserves.
-
D.
Morón
Morón is a city in the western part of the Greater Buenos Aires metropolitan area in Argentina, known as an important residential and commercial hub.
-
E.
Tres de Febrero
chosen
Tres de Febrero is a partido (administrative district) in the Greater Buenos Aires metropolitan area of Argentina, known for its dense urban character and proximity to the city of Buenos Aires.
- 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_69ca82c4538081909404325aa5639483 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb4c4c2e388190b86854f8b1765e61 |
completed | March 31, 2026, 4:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce4d731b248190a440e1289e655b74 |
completed | April 2, 2026, 11:05 a.m. |
Created at: March 30, 2026, 5:40 p.m.