Triple
T37035402
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PEP 647 |
E916620
|
entity |
| Predicate | relatesToLanguage |
P87230
|
FINISHED |
| Object | Python |
E3372
|
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 | Statement: [PEP 647, relatesToLanguage, Python]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relatesToLanguage Context triple: [PEP 647, relatesToLanguage, Python]
-
A.
linkedToLanguage
chosen
Indicates that an entity has an association or connection with a specific language, such as being expressed in, related to, or dependent on that language.
-
B.
hasRelatedLanguage
Indicates that one language is related to another through shared linguistic origins, features, or classification.
-
C.
linguisticallyRelatedTo
Indicates that two entities are connected through a linguistic relationship, such as sharing a common language, origin, structure, or other language-based association.
-
D.
reflectsLanguage
Indicates that one entity expresses, embodies, or reveals the language or linguistic characteristics associated with another entity.
-
E.
refersToLanguageSpokenIn
Indicates that one entity designates or mentions the language that is spoken in another entity (such as a place or region).
- F. None of above.
Provenance (4 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_69f76e93ec4c8190be81cf87354d9155 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a037c8e2c648190a65fc9c7872861af |
completed | May 12, 2026, 7:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a3e8c3a0e588190b205ae10f829e5c5 |
completed | June 26, 2026, 2:27 p.m. |
| PD | Predicate disambiguation | batch_6a037a10036481909c71188b2a0e7f04 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:14 p.m.