Triple
T6556211
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line 8 (Madrid Metro) |
E152453
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Mar de Cristal
Mar de Cristal is a Madrid Metro station serving as an interchange between lines 4 and 8 in the northeastern part of the city.
|
E604804
|
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: Mar de Cristal | Statement: [Line 8 (Madrid Metro), hasStation, Mar de Cristal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mar de Cristal Context triple: [Line 8 (Madrid Metro), hasStation, Mar de Cristal]
-
A.
Agujas Grandes
Agujas Grandes is the prominent peak that forms the main mountainous landmark on the Canary Island of La Graciosa, Spain.
-
B.
Valle del Marina
Valle del Marina is a valley in the Tuscany region of central Italy, known for encompassing the municipality of Calenzano near Florence.
-
C.
Talacauvery
Talacauvery is a revered pilgrimage site in Karnataka, India, regarded as the source of the river Kaveri and home to a prominent temple dedicated to the goddess.
-
D.
Cogua
Cogua is a municipality in the Cundinamarca Department of Colombia, located on the Bogotá savanna north of the capital.
-
E.
Cieneguilla
Cieneguilla is a semi-rural district in the eastern part of Lima, Peru, known for its natural landscapes, country houses, and outdoor recreation areas.
- 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: Mar de Cristal Triple: [Line 8 (Madrid Metro), hasStation, Mar de Cristal]
Generated description
Mar de Cristal is a Madrid Metro station serving as an interchange between lines 4 and 8 in the northeastern part of the city.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mar de Cristal Target entity description: Mar de Cristal is a Madrid Metro station serving as an interchange between lines 4 and 8 in the northeastern part of the city.
-
A.
Agujas Grandes
Agujas Grandes is the prominent peak that forms the main mountainous landmark on the Canary Island of La Graciosa, Spain.
-
B.
Valle del Marina
Valle del Marina is a valley in the Tuscany region of central Italy, known for encompassing the municipality of Calenzano near Florence.
-
C.
Talacauvery
Talacauvery is a revered pilgrimage site in Karnataka, India, regarded as the source of the river Kaveri and home to a prominent temple dedicated to the goddess.
-
D.
Cogua
Cogua is a municipality in the Cundinamarca Department of Colombia, located on the Bogotá savanna north of the capital.
-
E.
Cieneguilla
Cieneguilla is a semi-rural district in the eastern part of Lima, Peru, known for its natural landscapes, country houses, and outdoor recreation areas.
- 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_69c6d559ad4881909c1e7712d84945f6 |
completed | March 27, 2026, 7:07 p.m. |
| NEDg | Description generation | batch_69c6d676e43081909bf2a9cceff0b9b3 |
completed | March 27, 2026, 7:11 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c6d84076d48190ada0903af49613de |
completed | March 27, 2026, 7:19 p.m. |
Created at: March 27, 2026, 1:51 p.m.