Triple
T4554918
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Verizon Media |
E120457
|
entity |
| Predicate | ownedBrand |
P1500
|
FINISHED |
| Object | Engadget |
E66252
|
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: Engadget | Statement: [Verizon Media, ownedBrand, Engadget]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Engadget Context triple: [Verizon Media, ownedBrand, Engadget]
-
A.
Engadget
chosen
Engadget is a technology news and reviews website that covers consumer electronics, gadgets, and digital culture.
-
B.
Tekno
Tekno is a Nigerian singer, songwriter, and record producer known for his Afrobeat and Afropop hit songs and dance-oriented sound.
-
C.
MWC
MWC is the commonly used abbreviation for Mennonite World Conference, a global community and fellowship of Anabaptist-related churches.
-
D.
Autoblog
Autoblog is an automotive news and review website known for its coverage of car industry news, vehicle reviews, and consumer car-buying information.
-
E.
Wired magazine
Wired magazine is an American technology and culture publication known for its in-depth coverage of digital innovation, science, and the impact of emerging technologies on society.
- 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_69bd4636f1648190a701445c2fcd9c17 |
completed | March 20, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69bd5813af948190b10b02dadf6496bf |
completed | March 20, 2026, 2:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdc575e3388190ac95b9e0537fb701 |
completed | March 20, 2026, 10:08 p.m. |
Created at: March 20, 2026, 1:09 p.m.