Triple
T10769690
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line 5 (Barcelona Metro) |
E254041
|
entity |
| Predicate | connectsStation |
P845
|
FINISHED |
| Object |
Can Boixeres
Can Boixeres is a Barcelona Metro station on the city's rapid transit network, serving the local area as part of the system’s Line 5 corridor.
|
E884812
|
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 Boixeres | Statement: [Line 5 (Barcelona Metro), connectsStation, Can Boixeres]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Can Boixeres Context triple: [Line 5 (Barcelona Metro), connectsStation, Can Boixeres]
-
A.
Barradères
Barradères is a coastal commune and fishing town in Haiti located within the Nippes Department.
-
B.
Noguès
Noguès is a French surname borne by various notable individuals, including figures in politics, arts, and sports.
-
C.
Beineix
Beineix is the surname of French film director Jean-Jacques Beineix, known for visually stylish works like "Diva" and "Betty Blue."
-
D.
Conségudes
Conségudes is a small commune in the Alpes-Maritimes department of southeastern France.
-
E.
Le Suquet
Le Suquet is the historic old quarter of Cannes, known for its steep cobbled streets, medieval architecture, and panoramic views over the city and harbor.
- 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 Boixeres Triple: [Line 5 (Barcelona Metro), connectsStation, Can Boixeres]
Generated description
Can Boixeres is a Barcelona Metro station on the city's rapid transit network, serving the local area as part of the system’s Line 5 corridor.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Can Boixeres Target entity description: Can Boixeres is a Barcelona Metro station on the city's rapid transit network, serving the local area as part of the system’s Line 5 corridor.
-
A.
Barradères
Barradères is a coastal commune and fishing town in Haiti located within the Nippes Department.
-
B.
Noguès
Noguès is a French surname borne by various notable individuals, including figures in politics, arts, and sports.
-
C.
Beineix
Beineix is the surname of French film director Jean-Jacques Beineix, known for visually stylish works like "Diva" and "Betty Blue."
-
D.
Conségudes
Conségudes is a small commune in the Alpes-Maritimes department of southeastern France.
-
E.
Le Suquet
Le Suquet is the historic old quarter of Cannes, known for its steep cobbled streets, medieval architecture, and panoramic views over the city and harbor.
- 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.