Triple
T6556214
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line 8 (Madrid Metro) |
E152453
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Barajas
Barajas is a Madrid Metro station on Line 8 that serves the Barajas district near Madrid–Barajas Airport.
|
E610092
|
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: Barajas | Statement: [Line 8 (Madrid Metro), hasStation, Barajas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Barajas Context triple: [Line 8 (Madrid Metro), hasStation, Barajas]
-
A.
Paterna
Paterna is a municipality in eastern Spain known for its proximity to the city of Valencia and its mix of industrial activity and residential areas.
-
B.
Baeza
Baeza is a historic Andalusian town in southern Spain renowned for its well-preserved Renaissance architecture and status as a UNESCO World Heritage Site.
-
C.
Lebrija
Lebrija is a historic town and municipality in southern Spain’s Andalusia region, known for its agricultural economy and traditional flamenco culture.
-
D.
Escalona
Escalona is a historic Spanish town whose name is associated with the noble title of Duke of Escalona.
-
E.
Alhué
Alhué is a rural commune and town in central Chile known for its agricultural activities and traditional countryside character within the Santiago Metropolitan Region.
- 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: Barajas Triple: [Line 8 (Madrid Metro), hasStation, Barajas]
Generated description
Barajas is a Madrid Metro station on Line 8 that serves the Barajas district near Madrid–Barajas Airport.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Barajas Target entity description: Barajas is a Madrid Metro station on Line 8 that serves the Barajas district near Madrid–Barajas Airport.
-
A.
Paterna
Paterna is a municipality in eastern Spain known for its proximity to the city of Valencia and its mix of industrial activity and residential areas.
-
B.
Baeza
Baeza is a historic Andalusian town in southern Spain renowned for its well-preserved Renaissance architecture and status as a UNESCO World Heritage Site.
-
C.
Lebrija
Lebrija is a historic town and municipality in southern Spain’s Andalusia region, known for its agricultural economy and traditional flamenco culture.
-
D.
Escalona
Escalona is a historic Spanish town whose name is associated with the noble title of Duke of Escalona.
-
E.
Alhué
Alhué is a rural commune and town in central Chile known for its agricultural activities and traditional countryside character within the Santiago Metropolitan Region.
- 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_69c688058d6881908c19b309cc55dbfa |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6ae1d28bc8190a2fa4b3e1e39863c |
completed | March 27, 2026, 4:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6eed99a1c8190b37da0ffed24e203 |
completed | March 27, 2026, 8:55 p.m. |
| NEDg | Description generation | batch_69c6f09ea58c8190bfd8a183581b5a5a |
completed | March 27, 2026, 9:03 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c6f1a0935881908afc30ce76bdf76f |
completed | March 27, 2026, 9:07 p.m. |
Created at: March 27, 2026, 1:51 p.m.