Triple
T8929606
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SEAT Martorell plant |
E212617
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object |
Martorell
Martorell is a town in Catalonia, Spain, known as an important industrial hub within the Barcelona metropolitan area.
|
E767182
|
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: Martorell | Statement: [SEAT Martorell plant, locatedIn, Martorell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Martorell Context triple: [SEAT Martorell plant, locatedIn, Martorell]
-
A.
Banyoles
Banyoles is a town in Catalonia, Spain, best known for its large natural lake and scenic surroundings.
-
B.
Ampurias
Ampurias (Empúries) was an ancient Greek and later Roman coastal settlement in northeastern Spain that became an important trading hub in the western Mediterranean.
-
C.
Deià
Deià is a picturesque coastal village on the Spanish island of Mallorca, famed for its dramatic mountain-and-sea scenery and its long association with artists and writers.
-
D.
Mataró
Mataró is a coastal city in northeastern Spain known as an important commercial and industrial center on the Mediterranean near Barcelona.
-
E.
Tàrrega
Tàrrega is a historic town in Catalonia, Spain, known for its cultural festivals and medieval heritage.
- 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: Martorell Triple: [SEAT Martorell plant, locatedIn, Martorell]
Generated description
Martorell is a town in Catalonia, Spain, known as an important industrial hub within the Barcelona metropolitan area.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Martorell Target entity description: Martorell is a town in Catalonia, Spain, known as an important industrial hub within the Barcelona metropolitan area.
-
A.
Banyoles
Banyoles is a town in Catalonia, Spain, best known for its large natural lake and scenic surroundings.
-
B.
Ampurias
Ampurias (Empúries) was an ancient Greek and later Roman coastal settlement in northeastern Spain that became an important trading hub in the western Mediterranean.
-
C.
Deià
Deià is a picturesque coastal village on the Spanish island of Mallorca, famed for its dramatic mountain-and-sea scenery and its long association with artists and writers.
-
D.
Mataró
Mataró is a coastal city in northeastern Spain known as an important commercial and industrial center on the Mediterranean near Barcelona.
-
E.
Tàrrega
Tàrrega is a historic town in Catalonia, Spain, known for its cultural festivals and medieval heritage.
- 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_69ca8395c438819087d7cb844ab5990c |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc667470308190a75ba63de803e3a2 |
completed | April 1, 2026, 12:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfc1d55d84819094bc2b6e3dd94254 |
completed | April 3, 2026, 1:34 p.m. |
| NEDg | Description generation | batch_69cfc25fdf3481909d9821f7728b0c5b |
completed | April 3, 2026, 1:36 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cfc2e808408190b9bc44ed21fc67d9 |
completed | April 3, 2026, 1:38 p.m. |
Created at: March 30, 2026, 6:57 p.m.