Triple
T1774227
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SimpleText |
E38941
|
entity |
| Predicate | predecessor |
P97
|
FINISHED |
| Object | TeachText |
E38942
|
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: TeachText | Statement: [SimpleText, predecessor, TeachText]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TeachText Context triple: [SimpleText, predecessor, TeachText]
-
A.
TeachText
chosen
TeachText was a simple text-editing application bundled with early versions of the classic Mac OS, primarily used for reading documentation and creating basic text files.
-
B.
Immersive Reader
Immersive Reader is a Microsoft tool that enhances reading comprehension and accessibility by simplifying page layouts, reading text aloud, and offering customizable reading preferences.
-
C.
C-text
C-text is one of the principal textual versions of the Middle English allegorical poem *Piers Plowman*, representing a distinct editorial and manuscript tradition within its complex transmission history.
-
D.
TextEdit
TextEdit is a simple, built-in macOS application for creating and editing plain text and rich text documents.
-
E.
SimpleText
SimpleText was a basic text-editing and word-processing application bundled with classic Mac OS, providing users with simple tools for creating and editing plain and styled text documents.
- 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_69a8862e61708190af97b9838cc3f5de |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa64b59428819082e0d43a61f4f299 |
completed | March 6, 2026, 5:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ada9982d208190b0c29ee1141e91b0 |
completed | March 8, 2026, 4:53 p.m. |
Created at: March 4, 2026, 7:31 p.m.