Triple
T35305365
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lisbon suburban rail network |
E1019620
|
entity |
| Predicate | connectsTo |
P845
|
FINISHED |
| Object |
fertagus rail services
Fertagus Rail Services is a private commuter rail operator in the Lisbon metropolitan area, best known for running trains across the 25 de Abril Bridge between Lisbon and the Setúbal Peninsula.
|
E2135773
|
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: fertagus rail services | Statement: [Lisbon suburban rail network, connectsTo, fertagus rail services]
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: fertagus rail services Triple: [Lisbon suburban rail network, connectsTo, fertagus rail services]
Generated description
Fertagus Rail Services is a private commuter rail operator in the Lisbon metropolitan area, best known for running trains across the 25 de Abril Bridge between Lisbon and the Setúbal Peninsula.
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_69f76de8b4c48190ae504b86185c474c |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f7904ec4dc8190bebc92e83e5a0a95 |
completed | May 3, 2026, 6:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a3819edb7488190b6039f8a34756ad9 |
completed | June 21, 2026, 5:05 p.m. |
| NEDg | Description generation | batch_6a381b16bb748190ad9ff7c8683dbf96 |
completed | June 21, 2026, 5:10 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a381f0ac04c81908d5630d9b3f7308f |
completed | June 21, 2026, 5:27 p.m. |
Created at: May 3, 2026, 4:03 p.m.