Triple
T4564192
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tyro |
E121868
|
entity |
| Predicate | grandmotherOf |
P3524
|
FINISHED |
| Object | Aeson |
E234809
|
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: Aeson | Statement: [Tyro, grandmotherOf, Aeson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aeson Context triple: [Tyro, grandmotherOf, Aeson]
-
A.
Aeson
chosen
Aeson is a figure in Greek mythology best known as the king of Iolcus and the father of the hero Jason, leader of the Argonauts.
-
B.
HJSON
HJSON is a human-friendly extension of JSON that relaxes its strict syntax to allow comments, unquoted keys, and other conveniences for easier configuration editing.
-
C.
JSON5
JSON5 is an extension of the JSON data format that adds more human-friendly features like comments, trailing commas, and unquoted object keys while remaining largely compatible with standard JSON.
-
D.
JSON
JSON (JavaScript Object Notation) is a lightweight, text-based data interchange format widely used for transmitting structured data in web APIs and configuration files.
-
E.
Haskell
Haskell is a statically typed, purely functional programming language known for its strong type system, lazy evaluation, and use in both academic research and industry.
- 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_69bd463f156881908a99aca69c5721ac |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd589b439c81908da9d19433310bcd |
completed | March 20, 2026, 2:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdc5a8ea5481908a652290e9df911f |
completed | March 20, 2026, 10:09 p.m. |
Created at: March 20, 2026, 1:09 p.m.