Triple
T10769691
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line 5 (Barcelona Metro) |
E254041
|
entity |
| Predicate | connectsStation |
P845
|
FINISHED |
| Object |
Can Vidalet
Can Vidalet is a Barcelona Metro station on the city's Line 5 serving the Esplugues de Llobregat area.
|
E884813
|
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: Can Vidalet | Statement: [Line 5 (Barcelona Metro), connectsStation, Can Vidalet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Can Vidalet Context triple: [Line 5 (Barcelona Metro), connectsStation, Can Vidalet]
-
A.
Visperad
Visperad is a Zoroastrian liturgical text and ceremony that expands upon the Yasna ritual with additional invocations to various divine beings.
-
B.
VIAL
VIAL is the ICAO airport code assigned to Prayagraj Airport in Uttar Pradesh, India.
-
C.
VITA
VITA is an industry trade association that develops and promotes open standards for embedded computing systems, particularly those based on the VMEbus architecture.
-
D.
Vuse
Vuse is an electronic cigarette and vaping product brand owned by British American Tobacco, known for its range of nicotine e-liquids and devices.
-
E.
Biktarvy
Biktarvy is a prescription combination antiretroviral medication used to treat HIV-1 infection in adults and certain children.
- 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: Can Vidalet Triple: [Line 5 (Barcelona Metro), connectsStation, Can Vidalet]
Generated description
Can Vidalet is a Barcelona Metro station on the city's Line 5 serving the Esplugues de Llobregat area.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Can Vidalet Target entity description: Can Vidalet is a Barcelona Metro station on the city's Line 5 serving the Esplugues de Llobregat area.
-
A.
Visperad
Visperad is a Zoroastrian liturgical text and ceremony that expands upon the Yasna ritual with additional invocations to various divine beings.
-
B.
VIAL
VIAL is the ICAO airport code assigned to Prayagraj Airport in Uttar Pradesh, India.
-
C.
VITA
VITA is an industry trade association that develops and promotes open standards for embedded computing systems, particularly those based on the VMEbus architecture.
-
D.
Vuse
Vuse is an electronic cigarette and vaping product brand owned by British American Tobacco, known for its range of nicotine e-liquids and devices.
-
E.
Biktarvy
Biktarvy is a prescription combination antiretroviral medication used to treat HIV-1 infection in adults and certain children.
- 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_69d6aa5f54f4819082d0bbcb6f8797e6 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d732307fb88190ba1447f68523c58a |
completed | April 9, 2026, 4:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69de23798af48190874d7e12c5155913 |
completed | April 14, 2026, 11:22 a.m. |
| NEDg | Description generation | batch_69de271fb08c8190a44c547083226fd8 |
completed | April 14, 2026, 11:38 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69de2cecc24c8190a240366e0600426a |
completed | April 14, 2026, 12:02 p.m. |
Created at: April 8, 2026, 9:16 p.m.