Triple
T15299363
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | M2 (Lausanne Metro) |
E365743
|
entity |
| Predicate | operator |
P179
|
FINISHED |
| Object |
TL
TL is the public transport operator serving the Lausanne region in Switzerland, managing the city’s metro, bus, and related transit services.
|
E1149169
|
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: TL | Statement: [M2 (Lausanne Metro), operator, TL]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TL Context triple: [M2 (Lausanne Metro), operator, TL]
-
A.
TL
TL is the vehicle registration code used on license plates for vehicles registered in Tulcea County, Romania.
-
B.
LT
LT is a mid-level trim designation commonly used by Chevrolet to denote a better-equipped, more comfort- and feature-focused version of its vehicles.
-
C.
LT
LT is the abbreviated name for the Logic Theorist, an early computer program that pioneered automated theorem proving in mathematical logic.
-
D.
TLF
TLF is the abbreviation commonly used for the Turkish Land Forces, the main ground warfare branch of Turkey’s military.
-
E.
TA
TA is a common abbreviation for the Territorial Army, a volunteer reserve force that supports a country's regular armed forces.
- 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: TL Triple: [M2 (Lausanne Metro), operator, TL]
Generated description
TL is the public transport operator serving the Lausanne region in Switzerland, managing the city’s metro, bus, and related transit services.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: TL Target entity description: TL is the public transport operator serving the Lausanne region in Switzerland, managing the city’s metro, bus, and related transit services.
-
A.
TL
TL is the vehicle registration code used on license plates for vehicles registered in Tulcea County, Romania.
-
B.
LT
LT is a mid-level trim designation commonly used by Chevrolet to denote a better-equipped, more comfort- and feature-focused version of its vehicles.
-
C.
LT
LT is the abbreviated name for the Logic Theorist, an early computer program that pioneered automated theorem proving in mathematical logic.
-
D.
TLF
TLF is the abbreviation commonly used for the Turkish Land Forces, the main ground warfare branch of Turkey’s military.
-
E.
TA
TA is a common abbreviation for the Territorial Army, a volunteer reserve force that supports a country's regular armed forces.
- 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_69d85a113ee881908e297a1d38dd79fa |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03686bfb8819080ba0caae652170a |
completed | April 16, 2026, 1:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69feef8513a08190b2d2a7dde85dd43d |
completed | May 9, 2026, 8:25 a.m. |
| NEDg | Description generation | batch_69fef23de4688190beeb59ef43891e3d |
completed | May 9, 2026, 8:37 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fef2d8fe04819084bb3deb6859d746 |
completed | May 9, 2026, 8:39 a.m. |
Created at: April 10, 2026, 3:15 a.m.