Triple
T7481018
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Red Line (Lisbon Metro) |
E176756
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Aeroporto station
Aeroporto station is the Lisbon Metro stop that serves Lisbon Airport as the eastern terminus of the system’s Red Line.
|
E666697
|
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: Aeroporto station | Statement: [Red Line (Lisbon Metro), hasStation, Aeroporto station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aeroporto station Context triple: [Red Line (Lisbon Metro), hasStation, Aeroporto station]
-
A.
Flughafen station
Flughafen station is the Nuremberg U-Bahn station that serves Nuremberg Airport as the terminus of line U2.
-
B.
Hangares station
Hangares station is a Mexico City Metro station on Line 5 located near the city's airport and serving the Venustiano Carranza borough.
-
C.
Aeroport metro station
Aeroport metro station is a Moscow Metro station on the Zamoskvoretskaya Line, serving the Aeroport District in the city’s north.
-
D.
Universitet station
Universitet station is a Moscow Metro station named after the nearby Moscow State University, serving passengers on the Sokolnicheskaya Line.
-
E.
Legarda station
Legarda station is an elevated rapid transit stop on Manila’s LRT Line 2 serving the Sampaloc area and nearby universities.
- 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: Aeroporto station Triple: [Red Line (Lisbon Metro), hasStation, Aeroporto station]
Generated description
Aeroporto station is the Lisbon Metro stop that serves Lisbon Airport as the eastern terminus of the system’s Red Line.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Aeroporto station Target entity description: Aeroporto station is the Lisbon Metro stop that serves Lisbon Airport as the eastern terminus of the system’s Red Line.
-
A.
Flughafen station
Flughafen station is the Nuremberg U-Bahn station that serves Nuremberg Airport as the terminus of line U2.
-
B.
Hangares station
Hangares station is a Mexico City Metro station on Line 5 located near the city's airport and serving the Venustiano Carranza borough.
-
C.
Aeroport metro station
Aeroport metro station is a Moscow Metro station on the Zamoskvoretskaya Line, serving the Aeroport District in the city’s north.
-
D.
Universitet station
Universitet station is a Moscow Metro station named after the nearby Moscow State University, serving passengers on the Sokolnicheskaya Line.
-
E.
Legarda station
Legarda station is an elevated rapid transit stop on Manila’s LRT Line 2 serving the Sampaloc area and nearby universities.
- 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_69c69f236ce08190a04d7679f03b29b2 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f534aa388190b3bb3e16be3a54c8 |
completed | March 27, 2026, 9:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c83493f0988190b8c569d34bbe2817 |
completed | March 28, 2026, 8:05 p.m. |
| NEDg | Description generation | batch_69c835cf7a58819095ed79b84f935a84 |
completed | March 28, 2026, 8:10 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8366b2fcc8190bd8b07a54a848721 |
completed | March 28, 2026, 8:13 p.m. |
Created at: March 27, 2026, 3:42 p.m.