Triple
T18051832
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Click |
E431940
|
entity |
| Predicate | alternativeTo |
P5887
|
FINISHED |
| Object | Typer |
—
|
NE NERFINISHED |
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: Typer | Statement: [Click, alternativeTo, Typer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Typer Context triple: [Click, alternativeTo, Typer]
-
A.
Typer
chosen
Typer is a modern, user-friendly Python library for building command-line interfaces, created by Sebastián Ramírez (tiangolo), that emphasizes type hints and automatic documentation.
-
B.
Tyros
Tyros is a coastal town in the traditional Tsakonian region of the eastern Peloponnese in Greece, known for its beaches and local maritime heritage.
-
C.
typer
Typer is a modern, user-friendly Python library for building command-line interfaces, created by Sebastián Ramírez (tiangolo), the author of FastAPI.
-
D.
Tyto
Tyto is a genus of medium-sized owls best known for including the widespread barn owl and its close relatives.
-
E.
Tyltyl
Tyltyl is the young protagonist of Maurice Maeterlinck’s play "The Blue Bird," who embarks on a magical quest in search of happiness.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b906482481908183315b9ecf9994 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4c0fe4f1881908fa8485cb3ccfa44 |
completed | April 19, 2026, 11:48 a.m. |
Created at: April 10, 2026, 10:25 a.m.