Triple
T14513856
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baelor |
E340465
|
entity |
| Predicate | productionCompany |
P490
|
FINISHED |
| Object | Grok! Television |
E340458
|
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: Grok! Television | Statement: [Baelor, productionCompany, Grok! Television]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Grok! Television Context triple: [Baelor, productionCompany, Grok! Television]
-
A.
Grok! Television
chosen
Grok! Television is a production company known for its involvement in creating the first season of the acclaimed fantasy television series "Game of Thrones."
-
B.
Grok
Grok is an AI chatbot developed by xAI, designed to provide conversational access to real-time information and reasoning capabilities.
-
C.
Mr. Television
Mr. Television is the nickname of Milton Berle, a pioneering American comedian and actor who became one of the first major stars of early television.
-
D.
Grok (web framework)
Grok is a Python-based web framework that emphasizes convention over configuration and rapid development, built on top of the Zope toolkit.
-
E.
Gopher Cam
Gopher Cam is a specialized in-track camera system used in NASCAR broadcasts to provide low-angle, on-the-ground racing footage.
- 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_69d822d9c0408190b9a2b3643e58bb4d |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69de9a6d82988190b6f957012bcc63d4 |
completed | April 14, 2026, 7:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd6da64db881909a4f88d18031cb0c |
completed | May 8, 2026, 4:59 a.m. |
Created at: April 10, 2026, 1:21 a.m.