Triple
T16326973
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Société de transport de Laval |
E396446
|
entity |
| Predicate | hasAbbreviation |
P43
|
FINISHED |
| Object |
STL
STL is the public transit agency serving the city of Laval in Quebec, Canada, operating bus and related transportation services.
|
E1207355
|
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: STL | Statement: [Société de transport de Laval, hasAbbreviation, STL]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: STL Context triple: [Société de transport de Laval, hasAbbreviation, STL]
-
A.
STL
STL is a common abbreviation and nickname for the city of St. Louis, Missouri.
-
B.
STL
STL is a German vehicle registration code assigned to the Erzgebirgskreis district in the state of Saxony.
-
C.
STLAM
STLAM is the stock ticker symbol for Stellantis, a multinational automotive manufacturer formed from the merger of Fiat Chrysler Automobiles and PSA Group.
-
D.
Effective STL
Effective STL is a programming book by Scott Meyers that provides practical guidelines and best practices for using the C++ Standard Template Library effectively and efficiently.
-
E.
STLR
STLR is a student-run law journal at Stanford Law School that focuses on legal issues arising from science, technology, and innovation.
- 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: STL Triple: [Société de transport de Laval, hasAbbreviation, STL]
Generated description
STL is the public transit agency serving the city of Laval in Quebec, Canada, operating bus and related transportation services.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: STL Target entity description: STL is the public transit agency serving the city of Laval in Quebec, Canada, operating bus and related transportation services.
-
A.
STL
STL is a common abbreviation and nickname for the city of St. Louis, Missouri.
-
B.
STL
STL is a German vehicle registration code assigned to the Erzgebirgskreis district in the state of Saxony.
-
C.
STLAM
STLAM is the stock ticker symbol for Stellantis, a multinational automotive manufacturer formed from the merger of Fiat Chrysler Automobiles and PSA Group.
-
D.
Effective STL
Effective STL is a programming book by Scott Meyers that provides practical guidelines and best practices for using the C++ Standard Template Library effectively and efficiently.
-
E.
STLR
STLR is a student-run law journal at Stanford Law School that focuses on legal issues arising from science, technology, and innovation.
- 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_69d87f255b788190a400eba031dd85d8 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e296bab8b48190b373b4efbd6f0d8c |
completed | April 17, 2026, 8:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00260f487c81909e3e54e47c11b83a |
completed | May 10, 2026, 6:30 a.m. |
| NEDg | Description generation | batch_6a0027f09b588190b71d550d2a14868d |
completed | May 10, 2026, 6:38 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a002899c8888190be247f5db60552e0 |
completed | May 10, 2026, 6:41 a.m. |
Created at: April 10, 2026, 5:07 a.m.