Triple
T7075996
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Madrid Metro Line 1 |
E164819
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Tetuán
Tetuán is a station on the Madrid Metro network serving the Tetuán district in the north of Spain’s capital.
|
E639934
|
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: Tetuán | Statement: [Madrid Metro Line 1, hasStation, Tetuán]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tetuán Context triple: [Madrid Metro Line 1, hasStation, Tetuán]
-
A.
El Azbakeya
El Azbakeya is a historic district in central Cairo known for its cultural landmarks, markets, and longstanding role as an urban hub of the city.
-
B.
Xàtiva
Xàtiva is a historic town in the Valencian Community of Spain, known for its medieval castle, rich cultural heritage, and role as the birthplace of the Borgia family.
-
C.
Arganzuela district
Arganzuela district is a central district of Madrid, Spain, known for its mix of residential neighborhoods, cultural venues, and proximity to the Manzanares River.
-
D.
Melilla
Melilla is a Spanish autonomous city located on the north coast of Africa, bordering Morocco and serving as a key enclave between Europe and Africa.
-
E.
Abdiyya
Abdiyya is a female given name of Arabic origin, used in contexts such as royal and historical figures in the Arab world.
- 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: Tetuán Triple: [Madrid Metro Line 1, hasStation, Tetuán]
Generated description
Tetuán is a station on the Madrid Metro network serving the Tetuán district in the north of Spain’s capital.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tetuán Target entity description: Tetuán is a station on the Madrid Metro network serving the Tetuán district in the north of Spain’s capital.
-
A.
El Azbakeya
El Azbakeya is a historic district in central Cairo known for its cultural landmarks, markets, and longstanding role as an urban hub of the city.
-
B.
Xàtiva
Xàtiva is a historic town in the Valencian Community of Spain, known for its medieval castle, rich cultural heritage, and role as the birthplace of the Borgia family.
-
C.
Arganzuela district
Arganzuela district is a central district of Madrid, Spain, known for its mix of residential neighborhoods, cultural venues, and proximity to the Manzanares River.
-
D.
Melilla
Melilla is a Spanish autonomous city located on the north coast of Africa, bordering Morocco and serving as a key enclave between Europe and Africa.
-
E.
Abdiyya
Abdiyya is a female given name of Arabic origin, used in contexts such as royal and historical figures in the Arab world.
- 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_69c6887cbc6c8190bdfac42d940f4d8a |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e4ce3d3c81908cbb912b256aadbf |
completed | March 27, 2026, 8:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c79468c7688190bf10433f05e77574 |
completed | March 28, 2026, 8:42 a.m. |
| NEDg | Description generation | batch_69c79530c0588190826350a1cbcd5325 |
completed | March 28, 2026, 8:45 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c795b1be18819087a4a70fc567bd80 |
completed | March 28, 2026, 8:47 a.m. |
Created at: March 27, 2026, 2:40 p.m.