Triple
T12826684
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Linha do Vouga |
E306668
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Águeda station
Águeda station is a railway station in the city of Águeda, Portugal, serving passengers on the narrow-gauge Vouga Line.
|
E1006703
|
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: Águeda station | Statement: [Linha do Vouga, hasStation, Águeda station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Águeda station Context triple: [Linha do Vouga, hasStation, Águeda station]
-
A.
Medrano station
Medrano station is a stop on Buenos Aires’ Line B underground, serving the Almagro neighborhood near Avenida Medrano.
-
B.
Pedrero station
Pedrero station is a Santiago Metro stop in Chile located near the Estadio Monumental, serving passengers on the city’s Line 5.
-
C.
Talavera station
Talavera station is one of the intermediate stops on Wellington’s historic cable car line in New Zealand, serving nearby residential and university areas.
-
D.
Olleros station
Olleros station is a stop on Buenos Aires’ Line D subway, serving the Palermo and Colegiales neighborhoods in Argentina’s capital.
-
E.
Navas station
Navas station is a Barcelona Metro stop in the Sant Andreu district, serving local commuters on the city's Line 1 network.
- 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: Águeda station Triple: [Linha do Vouga, hasStation, Águeda station]
Generated description
Águeda station is a railway station in the city of Águeda, Portugal, serving passengers on the narrow-gauge Vouga Line.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Águeda station Target entity description: Águeda station is a railway station in the city of Águeda, Portugal, serving passengers on the narrow-gauge Vouga Line.
-
A.
Medrano station
Medrano station is a stop on Buenos Aires’ Line B underground, serving the Almagro neighborhood near Avenida Medrano.
-
B.
Pedrero station
Pedrero station is a Santiago Metro stop in Chile located near the Estadio Monumental, serving passengers on the city’s Line 5.
-
C.
Talavera station
Talavera station is one of the intermediate stops on Wellington’s historic cable car line in New Zealand, serving nearby residential and university areas.
-
D.
Olleros station
Olleros station is a stop on Buenos Aires’ Line D subway, serving the Palermo and Colegiales neighborhoods in Argentina’s capital.
-
E.
Navas station
Navas station is a Barcelona Metro stop in the Sant Andreu district, serving local commuters on the city's Line 1 network.
- 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_69d7bdf52b94819096d6f0ba4ab50a98 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96fae51608190a50970bd038359a5 |
completed | April 10, 2026, 9:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f69b99d9bc8190b67f73985c8f6768 |
completed | May 3, 2026, 12:49 a.m. |
| NEDg | Description generation | batch_69f69d48e6948190a13afe3b8943d877 |
completed | May 3, 2026, 12:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f69dfa2b8481908827025a28bfb056 |
completed | May 3, 2026, 12:59 a.m. |
Created at: April 9, 2026, 5:33 p.m.