Triple
T18118563
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DMG Media |
E433672
|
entity |
| Predicate | publisherOf |
P1760
|
FINISHED |
| Object |
Metro
Metro is a free, commuter-focused daily newspaper in the United Kingdom known for its concise coverage of news, entertainment, and lifestyle.
|
E1306670
|
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: Metro | Statement: [DMG Media, publisherOf, Metro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Metro Context triple: [DMG Media, publisherOf, Metro]
-
A.
Metro
Metro is the rapid transit system serving the Washington, D.C. metropolitan area, operated by the Washington Metropolitan Area Transit Authority (WMATA).
-
B.
Metro
Metro is the primary public transportation agency serving Los Angeles County, operating buses, light rail, subway, and other transit services across the region.
-
C.
Metro
"Metro" is a Russian disaster thriller film featuring Svetlana Khodchenkova in a prominent role, centered on a catastrophic flood in the Moscow subway system.
-
D.
Metro
Metro is a multinational wholesale and food retail company headquartered in Germany, operating cash-and-carry stores and serving professional customers worldwide.
-
E.
Metro
Metro is a city in the Indonesian province of Lampung on the island of Sumatra, known as one of the region’s key urban and educational centers.
- 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: Metro Triple: [DMG Media, publisherOf, Metro]
Generated description
Metro is a free, commuter-focused daily newspaper in the United Kingdom known for its concise coverage of news, entertainment, and lifestyle.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Metro Target entity description: Metro is a free, commuter-focused daily newspaper in the United Kingdom known for its concise coverage of news, entertainment, and lifestyle.
-
A.
Metro
chosen
Metro is a free, commuter-focused daily newspaper in the United Kingdom known for its concise coverage of news, entertainment, and lifestyle.
-
B.
Metro
Metro is the public transport brand used for bus and rail services across West Yorkshire, England.
-
C.
Metro
Metro is a city in the Indonesian province of Lampung on the island of Sumatra, known as one of the region’s key urban and educational centers.
-
D.
Metro
Metro is the rapid transit system serving the Washington, D.C. metropolitan area, operated by the Washington Metropolitan Area Transit Authority (WMATA).
-
E.
Metro
Metro is the professional alias of Metro Boomin, a prominent American record producer and DJ known for shaping the sound of modern hip-hop and trap music.
- F. None of above.
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_69d8b909e8cc81908df4cc2b8ea6d11f |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4ddd843e88190abbc173dbc9b450a |
completed | April 19, 2026, 1:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a03853fd2648190b081c6c25a350f7d |
completed | May 12, 2026, 7:53 p.m. |
| NEDg | Description generation | batch_6a0388c5bea88190a3ced0fe4261aaa9 |
completed | May 12, 2026, 8:08 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a03894cfdfc8190bb8c0d229b8bf9ca |
completed | May 12, 2026, 8:10 p.m. |
Created at: April 10, 2026, 10:28 a.m.