Triple
T36884885
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | R3 line (Rodalies de Catalunya) |
E911581
|
entity |
| Predicate | servesStation |
P839
|
FINISHED |
| Object |
La Garriga railway station
La Garriga railway station is a commuter rail stop in Catalonia, Spain, forming part of the Rodalies de Catalunya network and serving the town of La Garriga.
|
E2204087
|
NE FINISHED |
How this triple was built (2 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: La Garriga railway station | Statement: [R3 line (Rodalies de Catalunya), servesStation, La Garriga railway station]
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: La Garriga railway station Triple: [R3 line (Rodalies de Catalunya), servesStation, La Garriga railway station]
Generated description
La Garriga railway station is a commuter rail stop in Catalonia, Spain, forming part of the Rodalies de Catalunya network and serving the town of La Garriga.
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_69f76e8335908190b77e7e11d0e80820 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69f9fd6d4ec48190942f3abf44bd056c |
completed | May 5, 2026, 2:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a3e161f9648819096543415e4da92be |
completed | June 26, 2026, 6:03 a.m. |
| NEDg | Description generation | batch_6a3e17d4fc6481908251350d7e38f187 |
completed | June 26, 2026, 6:10 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a3e18a200a08190aba68f4b8f64bf08 |
completed | June 26, 2026, 6:13 a.m. |
Created at: May 3, 2026, 4:13 p.m.