Triple
T14920952
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Italian motorway network |
E371505
|
entity |
| Predicate | electronicTollSystem |
P395
|
FINISHED |
| Object |
Telepass
Telepass is an Italian electronic toll collection system that allows drivers to pay motorway and other transport-related fees automatically without stopping at toll booths.
|
E1126525
|
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: Telepass | Statement: [Italian motorway network, electronicTollSystem, Telepass]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Telepass Context triple: [Italian motorway network, electronicTollSystem, Telepass]
-
A.
Etrek
Etrek is a town in the Balkan Region of southwestern Turkmenistan, near the border with Iran.
-
B.
iPASS
iPASS is a Taiwanese contactless smart card widely used for public transportation fares and small-value electronic payments.
-
C.
Telecip
Telecip is a French film production company best known for backing the 1976 Academy Award–winning war satire "Black and White in Color."
-
D.
Traficom
Traficom is Finland’s national authority responsible for regulating and overseeing transport and communications services, infrastructure, and safety.
-
E.
Telegin
Telegin is a minor but memorable character in Anton Chekhov’s play "Uncle Vanya," known for his shabby gentility, loyalty, and melancholy humor.
- 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: Telepass Triple: [Italian motorway network, electronicTollSystem, Telepass]
Generated description
Telepass is an Italian electronic toll collection system that allows drivers to pay motorway and other transport-related fees automatically without stopping at toll booths.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Telepass Target entity description: Telepass is an Italian electronic toll collection system that allows drivers to pay motorway and other transport-related fees automatically without stopping at toll booths.
-
A.
Etrek
Etrek is a town in the Balkan Region of southwestern Turkmenistan, near the border with Iran.
-
B.
iPASS
iPASS is a Taiwanese contactless smart card widely used for public transportation fares and small-value electronic payments.
-
C.
Telecip
Telecip is a French film production company best known for backing the 1976 Academy Award–winning war satire "Black and White in Color."
-
D.
Traficom
Traficom is Finland’s national authority responsible for regulating and overseeing transport and communications services, infrastructure, and safety.
-
E.
Telegin
Telegin is a minor but memorable character in Anton Chekhov’s play "Uncle Vanya," known for his shabby gentility, loyalty, and melancholy humor.
- 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_69d85cc7ea3481908228b5acb7d06f12 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded63121e48190b54eb2546acc3c93 |
completed | April 15, 2026, 12:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe72c141088190936f2fd53fee80e4 |
completed | May 8, 2026, 11:33 p.m. |
| NEDg | Description generation | batch_69fe7477270c819094a908a05143619d |
completed | May 8, 2026, 11:40 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe7542e5948190866991cabdf97f0e |
completed | May 8, 2026, 11:44 p.m. |
Created at: April 10, 2026, 2:33 a.m.