Triple
T7713819
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gawker Media |
E174831
|
entity |
| Predicate | owns |
P347
|
FINISHED |
| Object |
Lifehacker
Lifehacker is a technology and productivity blog that offers tips, tricks, and how-to guides to help readers optimize their daily lives and work.
|
E682565
|
NE FINISHED |
How this triple was built (4 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: Lifehacker | Statement: [Gawker Media, owns, Lifehacker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lifehacker Context triple: [Gawker Media, owns, Lifehacker]
-
A.
Wirecutter
Wirecutter is a product review and recommendation website known for its rigorously researched buying guides across a wide range of consumer goods.
-
B.
Fast Company
Fast Company is a leading American business media brand and magazine focused on innovation in technology, leadership, and design.
-
C.
Boing Boing
Boing Boing is a long-running, influential blog and online magazine that covers technology, culture, science fiction, and digital rights with a quirky, countercultural tone.
-
D.
Engadget
Engadget is a technology news and reviews website that covers consumer electronics, gadgets, and digital culture.
-
E.
Lifestyle
Lifestyle is a section of The Guardian that covers topics such as culture, fashion, food, health, relationships, and everyday living.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Lifehacker Triple: [Gawker Media, owns, Lifehacker]
Generated description
Lifehacker is a technology and productivity blog that offers tips, tricks, and how-to guides to help readers optimize their daily lives and work.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lifehacker Target entity description: Lifehacker is a technology and productivity blog that offers tips, tricks, and how-to guides to help readers optimize their daily lives and work.
-
A.
Wirecutter
Wirecutter is a product review and recommendation website known for its rigorously researched buying guides across a wide range of consumer goods.
-
B.
Fast Company
Fast Company is a leading American business media brand and magazine focused on innovation in technology, leadership, and design.
-
C.
Boing Boing
Boing Boing is a long-running, influential blog and online magazine that covers technology, culture, science fiction, and digital rights with a quirky, countercultural tone.
-
D.
Engadget
Engadget is a technology news and reviews website that covers consumer electronics, gadgets, and digital culture.
-
E.
Lifestyle
Lifestyle is a section of The Guardian that covers topics such as culture, fashion, food, health, relationships, and everyday living.
- F. None of above. chosen
Provenance (5 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_69c6995c463c8190a14458036249d419 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c702ca8f048190a6ea27b8cee2f93e |
completed | March 27, 2026, 10:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8acd3e9f88190adf42ab42c21b722 |
completed | March 29, 2026, 4:38 a.m. |
| NEDg | Description generation | batch_69c8adbca88c819080cf255728a986b3 |
completed | March 29, 2026, 4:42 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8ae50d69881909530db8d874dace2 |
completed | March 29, 2026, 4:45 a.m. |
Created at: March 27, 2026, 4:04 p.m.