Triple
T17262709
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | محطة الدار البيضاء الميناء |
E419045
|
entity |
| Predicate | connectsTo |
P845
|
FINISHED |
| Object |
محطة فاس
محطة فاس هي محطة قطار رئيسية في مدينة فاس المغربية تُعد من أهم محاور النقل السككي بين شمال ووسط المملكة.
|
E1258441
|
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: محطة فاس | Statement: [محطة الدار البيضاء الميناء, connectsTo, محطة فاس]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: محطة فاس Context triple: [محطة الدار البيضاء الميناء, connectsTo, محطة فاس]
-
A.
Luz Station
Luz Station is a historic railway station and major transportation hub in São Paulo, Brazil, known for its distinctive architecture and cultural significance.
-
B.
Florenc station
Florenc station is a major interchange hub in the Prague Metro system, serving as a key transfer point between multiple lines and providing access to the city’s main bus terminal.
-
C.
Vinateros station
Vinateros station is a Madrid Metro station serving the Moratalaz district in Spain.
-
D.
Bellavista station
Bellavista station is a passenger rail stop on the Valparaíso Metro system serving the coastal city of Valparaíso, Chile.
-
E.
Piedras station
Piedras station is a stop on Buenos Aires’ historic Line A subway, serving the central Monserrat area near the city’s Plaza de Mayo.
- 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: محطة فاس Triple: [محطة الدار البيضاء الميناء, connectsTo, محطة فاس]
Generated description
محطة فاس هي محطة قطار رئيسية في مدينة فاس المغربية تُعد من أهم محاور النقل السككي بين شمال ووسط المملكة.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: محطة فاس Target entity description: محطة فاس هي محطة قطار رئيسية في مدينة فاس المغربية تُعد من أهم محاور النقل السككي بين شمال ووسط المملكة.
-
A.
Luz Station
Luz Station is a historic railway station and major transportation hub in São Paulo, Brazil, known for its distinctive architecture and cultural significance.
-
B.
Florenc station
Florenc station is a major interchange hub in the Prague Metro system, serving as a key transfer point between multiple lines and providing access to the city’s main bus terminal.
-
C.
Vinateros station
Vinateros station is a Madrid Metro station serving the Moratalaz district in Spain.
-
D.
Bellavista station
Bellavista station is a passenger rail stop on the Valparaíso Metro system serving the coastal city of Valparaíso, Chile.
-
E.
Piedras station
Piedras station is a stop on Buenos Aires’ historic Line A subway, serving the central Monserrat area near the city’s Plaza de Mayo.
- 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_69d886d9ab108190b70edd8d17aa1204 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42f4379848190add32ba8e5f93527 |
completed | April 19, 2026, 1:26 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0171041f2c81909bf52025d68912fc |
completed | May 11, 2026, 6:02 a.m. |
| NEDg | Description generation | batch_6a0171c1b5fc81908455cda0df277ea9 |
completed | May 11, 2026, 6:05 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a01724c4e34819099168d7303a31498 |
completed | May 11, 2026, 6:08 a.m. |
Created at: April 10, 2026, 5:40 a.m.