Triple
T10828067
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Python visual identity |
E255542
|
entity |
| Predicate | governsUseOf |
P760
|
FINISHED |
| Object | Python wordmark |
E51020
|
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: Python wordmark | Statement: [Python visual identity, governsUseOf, Python wordmark]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Python wordmark Context triple: [Python visual identity, governsUseOf, Python wordmark]
-
A.
Python wordmark
chosen
The Python wordmark is the official stylized text logo representing the Python programming language and its brand.
-
B.
Logos
Logos is a central concept in Christian theology referring to the divine Word or reason of God, identified with Christ as the preexistent and incarnate Son.
-
C.
wordmark quattro
The "wordmark quattro" is the stylized typographic logo used by Audi to denote its quattro all-wheel-drive technology.
-
D.
Logo
Logo is an educational programming language known for its turtle graphics, designed to help learners explore mathematical and computational ideas through simple commands.
-
E.
stylized wordmark "Junts"
The stylized wordmark "Junts" is the primary visual logo representing the Catalan political party Junts per Catalunya in its branding and communications.
- 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_69d6aa8081448190a9324184f2bd1c26 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d734d2b9f88190b79a7b168d7836c8 |
completed | April 9, 2026, 5:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69deb1096cbc81908f3eda562c2da042 |
completed | April 14, 2026, 9:26 p.m. |
Created at: April 8, 2026, 9:19 p.m.