Triple
T8411517
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line D (Buenos Aires Underground) |
E198633
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Juramento station
Juramento station is a stop on Buenos Aires’ Line D subway serving the Belgrano neighborhood.
|
E731531
|
NE FINISHED |
How this triple was built (4 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: Juramento station | Statement: [Line D (Buenos Aires Underground), hasStation, Juramento station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Juramento station Context triple: [Line D (Buenos Aires Underground), hasStation, Juramento station]
-
A.
Luz Station
Luz Station is a historic railway station and major transportation hub in São Paulo, Brazil, known for its distinctive architecture and cultural significance.
-
B.
La Estrella station
La Estrella station is the southern terminal station of Line A of the Medellín Metro system in Colombia.
-
C.
Legarda station
Legarda station is an elevated rapid transit stop on Manila’s LRT Line 2 serving the Sampaloc area and nearby universities.
-
D.
Salaryevo station
Salaryevo station is a Moscow Metro station on the Sokolnicheskaya Line serving the southwestern outskirts of the city.
-
E.
Quinta Normal station
Quinta Normal station is an underground stop on Santiago's Metro network that serves the Quinta Normal neighborhood and provides access to the nearby Quinta Normal Park and cultural institutions.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Juramento station Triple: [Line D (Buenos Aires Underground), hasStation, Juramento station]
Generated description
Juramento station is a stop on Buenos Aires’ Line D subway serving the Belgrano neighborhood.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Juramento station Target entity description: Juramento station is a stop on Buenos Aires’ Line D subway serving the Belgrano neighborhood.
-
A.
Luz Station
Luz Station is a historic railway station and major transportation hub in São Paulo, Brazil, known for its distinctive architecture and cultural significance.
-
B.
La Estrella station
La Estrella station is the southern terminal station of Line A of the Medellín Metro system in Colombia.
-
C.
Legarda station
Legarda station is an elevated rapid transit stop on Manila’s LRT Line 2 serving the Sampaloc area and nearby universities.
-
D.
Salaryevo station
Salaryevo station is a Moscow Metro station on the Sokolnicheskaya Line serving the southwestern outskirts of the city.
-
E.
Quinta Normal station
Quinta Normal station is an underground stop on Santiago's Metro network that serves the Quinta Normal neighborhood and provides access to the nearby Quinta Normal Park and cultural institutions.
- F. None of above. chosen
Provenance (5 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_69ca831201b481909e137936ef99ff11 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cb83e0341c819080506e696131671e |
completed | March 31, 2026, 8:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce0317cb188190b207bcaffb629a75 |
completed | April 2, 2026, 5:48 a.m. |
| NEDg | Description generation | batch_69ce07808098819087e896b87320aefd |
completed | April 2, 2026, 6:06 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ce08759e1c81909c96caf3b571e1ca |
completed | April 2, 2026, 6:11 a.m. |
Created at: March 30, 2026, 6:05 p.m.