Triple
T10117017
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | KSTL |
E223186
|
entity |
| Predicate | IATACode |
P418
|
FINISHED |
| Object | STL |
E34299
|
NE FINISHED |
How this triple was built (2 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: [KSTL, IATACode, STL]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: STL Context triple: [KSTL, IATACode, STL]
-
A.
STL
chosen
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.
C++
C++ is a high-performance, general-purpose programming language widely used for system/software development, game engines, and performance-critical applications.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ca8422047c81909d66b717b8b18cf3 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cdd162fac0819084c74947c1f6688e |
completed | April 2, 2026, 2:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2cc350310819096cca4cc251e3428 |
completed | April 5, 2026, 8:55 p.m. |
Created at: March 30, 2026, 9:04 p.m.