Triple
T10851732
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Assignment Expressions |
E256161
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object | walrus operator |
E256161
|
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: walrus operator | Statement: [Assignment Expressions, alsoKnownAs, walrus operator]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: walrus operator Context triple: [Assignment Expressions, alsoKnownAs, walrus operator]
-
A.
PEP 572
PEP 572 is the Python proposal that introduced the “walrus operator” (:=) for assignment expressions, allowing assignment within larger expressions.
-
B.
WAL
WAL is the official FIFA country code used to represent the Wales national football team in international competitions and records.
-
C.
WAL
WAL is the National Rail station code for Walton-on-Thames railway station in Surrey, England.
-
D.
Assignment Expressions
chosen
Assignment Expressions are a Python language feature introduced by PEP 572 that allow assigning values to variables as part of larger expressions using the “walrus” operator (:=).
-
E.
wal
"wal" is the ISO 639-2 language code for Wolaytta, an Omotic language spoken primarily in southwestern Ethiopia.
- 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_69d6aa81a5d08190aa86689061d1ddd2 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d75117b76c8190b0fb216b1428c3c7 |
completed | April 9, 2026, 7:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69deb17d978c8190883b4a56e88859de |
completed | April 14, 2026, 9:28 p.m. |
Created at: April 8, 2026, 9:20 p.m.