Triple
T10175975
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rufus Sewell |
E235851
|
entity |
| Predicate | theatreWork |
P27669
|
FINISHED |
| Object | Translations |
E561482
|
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: Translations | Statement: [Rufus Sewell, theatreWork, Translations]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Translations Context triple: [Rufus Sewell, theatreWork, Translations]
-
A.
Translations
chosen
Translations is a critically acclaimed play by Irish dramatist Brian Friel that explores themes of language, identity, and colonialism in 19th-century rural Ireland.
-
B.
Google Translate
Google Translate is a multilingual neural machine translation service by Google that instantly converts text, speech, images, and web pages between numerous languages.
-
C.
GTrans
GTrans is a public bus transit system serving the city of Gardena and surrounding areas in Los Angeles County, California.
-
D.
Yandex Translate
Yandex Translate is an online machine translation service by Yandex that supports multiple languages for text, website, and document translation.
-
E.
Microsoft Translator API
Microsoft Translator API is a cloud-based machine translation service that enables developers to add real-time, multilingual text and speech translation capabilities to their applications.
- 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_69ca84d1d5f88190ab878a1021ecff68 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdecd3a2688190bce277bffffcbf8b |
completed | April 2, 2026, 4:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d301232e5c8190ad80c4e78681994c |
completed | April 6, 2026, 12:41 a.m. |
Created at: March 30, 2026, 9:11 p.m.