Triple
T15365516
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lingotto (Turin Metro) |
E367402
|
entity |
| Predicate | adjacentStation |
P5707
|
FINISHED |
| Object |
Spezia (Turin Metro)
Spezia is a station on the Turin Metro system in Turin, Italy.
|
E1153411
|
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: Spezia (Turin Metro) | Statement: [Lingotto (Turin Metro), adjacentStation, Spezia (Turin Metro)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Spezia (Turin Metro) Context triple: [Lingotto (Turin Metro), adjacentStation, Spezia (Turin Metro)]
-
A.
Porta Nuova metro station
Porta Nuova metro station is a major Turin Metro stop integrated with the city’s main Porta Nuova railway station, serving as a key hub for urban and regional transit.
-
B.
Turin Metro
The Turin Metro is a fully automated, driverless rapid transit system serving the city of Turin, Italy.
-
C.
Brescia Metro
Brescia Metro is a fully automated light metro system serving the city of Brescia in northern Italy.
-
D.
Carlini Station
Carlini Station is an Argentine Antarctic research base on King George Island, focused on scientific studies of the polar environment and climate.
-
E.
Milan Metro Line 5
Milan Metro Line 5 is a fully automated, driverless metro line in Milan, Italy, running mainly in the northern part of the city and connecting key residential and transport hubs.
- 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: Spezia (Turin Metro) Triple: [Lingotto (Turin Metro), adjacentStation, Spezia (Turin Metro)]
Generated description
Spezia is a station on the Turin Metro system in Turin, Italy.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Spezia (Turin Metro) Target entity description: Spezia is a station on the Turin Metro system in Turin, Italy.
-
A.
Porta Nuova metro station
Porta Nuova metro station is a major Turin Metro stop integrated with the city’s main Porta Nuova railway station, serving as a key hub for urban and regional transit.
-
B.
Turin Metro
The Turin Metro is a fully automated, driverless rapid transit system serving the city of Turin, Italy.
-
C.
Brescia Metro
Brescia Metro is a fully automated light metro system serving the city of Brescia in northern Italy.
-
D.
Carlini Station
Carlini Station is an Argentine Antarctic research base on King George Island, focused on scientific studies of the polar environment and climate.
-
E.
Milan Metro Line 5
Milan Metro Line 5 is a fully automated, driverless metro line in Milan, Italy, running mainly in the northern part of the city and connecting key residential and transport hubs.
- 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_69d85a1483788190ad93c2748e8af34b |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e497de48190be249b110999ec5c |
completed | April 16, 2026, 1:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff0b4cc39c81908a0aff959352f6d5 |
completed | May 9, 2026, 10:24 a.m. |
| NEDg | Description generation | batch_69ff0df908848190b05c2ecf64f10b08 |
completed | May 9, 2026, 10:35 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff0e8f3b4481909642c91f1a54843c |
completed | May 9, 2026, 10:38 a.m. |
Created at: April 10, 2026, 3:18 a.m.