Triple
T14902430
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Budapest Cog-wheel Railway |
E360038
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object |
Line 60
Line 60 is the Budapest Cog-wheel Railway, a historic rack railway line in Budapest that connects the city’s hilly residential areas with the rest of the urban transport network.
|
E1126657
|
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: Line 60 | Statement: [Budapest Cog-wheel Railway, alsoKnownAs, Line 60]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Line 60 Context triple: [Budapest Cog-wheel Railway, alsoKnownAs, Line 60]
-
A.
Line 60
Line 60 is a railway line in Luxembourg that connects Luxembourg City with the southern industrial region, including towns such as Esch-sur-Alzette and Dudelange.
-
B.
Line 59
Line 59 is a Belgian railway line that connects the cities of Ghent and Antwerp.
-
C.
Line 50
Line 50 is a planned rapid transit line of the Shenzhen Metro system in Shenzhen, China.
-
D.
Line 40
Line 40 is a planned rapid transit line of the Shenzhen Metro system in Shenzhen, China.
-
E.
Line 46
Line 46 is a planned rapid transit line of the Shenzhen Metro system in Shenzhen, China.
- 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: Line 60 Triple: [Budapest Cog-wheel Railway, alsoKnownAs, Line 60]
Generated description
Line 60 is the Budapest Cog-wheel Railway, a historic rack railway line in Budapest that connects the city’s hilly residential areas with the rest of the urban transport network.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Line 60 Target entity description: Line 60 is the Budapest Cog-wheel Railway, a historic rack railway line in Budapest that connects the city’s hilly residential areas with the rest of the urban transport network.
-
A.
Line 60
Line 60 is a railway line in Luxembourg that connects Luxembourg City with the southern industrial region, including towns such as Esch-sur-Alzette and Dudelange.
-
B.
Line 59
Line 59 is a Belgian railway line that connects the cities of Ghent and Antwerp.
-
C.
Line 50
Line 50 is a planned rapid transit line of the Shenzhen Metro system in Shenzhen, China.
-
D.
Line 40
Line 40 is a planned rapid transit line of the Shenzhen Metro system in Shenzhen, China.
-
E.
Line 46
Line 46 is a planned rapid transit line of the Shenzhen Metro system in Shenzhen, China.
- 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_69d827980cbc8190a0c569ae3940a1d9 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69ded60b24008190bd272c0d61329400 |
completed | April 15, 2026, 12:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe72b4e4f88190af7e859d93dbbd28 |
completed | May 8, 2026, 11:33 p.m. |
| NEDg | Description generation | batch_69fe7360c11481908e2e5127b466e31b |
completed | May 8, 2026, 11:36 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe743c37308190a045ef5f0ade8508 |
completed | May 8, 2026, 11:39 p.m. |
Created at: April 10, 2026, 2:11 a.m.