Triple
T9614169
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | First Look Media |
E232176
|
entity |
| Predicate | hasBrand |
P1500
|
FINISHED |
| Object |
Topic
Topic is a streaming service and digital media brand known for curated, often international and socially conscious films, series, and documentaries.
|
E811071
|
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: Topic | Statement: [First Look Media, hasBrand, Topic]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Topic Context triple: [First Look Media, hasBrand, Topic]
-
A.
Tema
Tema is a major port and industrial city on the Atlantic coast of Ghana, located east of the capital Accra.
-
B.
Tema
Tema is a biblical figure mentioned in the Old Testament, traditionally regarded as a descendant of Ishmael and associated with a region or tribe in northwestern Arabia.
-
C.
Tema
Tema is a city located within Egypt's Sohag Governorate, known as a regional center in Upper Egypt.
-
D.
TOP
TOP is the IATA airport code for Philip Billard Municipal Airport serving Topeka, Kansas, in the United States.
-
E.
Hot Topics
Hot Topics is the opening discussion segment on the daytime talk show "The View," where the co-hosts debate and comment on current events and trending issues.
- 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: Topic Triple: [First Look Media, hasBrand, Topic]
Generated description
Topic is a streaming service and digital media brand known for curated, often international and socially conscious films, series, and documentaries.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Topic Target entity description: Topic is a streaming service and digital media brand known for curated, often international and socially conscious films, series, and documentaries.
-
A.
Tema
Tema is a major port and industrial city on the Atlantic coast of Ghana, located east of the capital Accra.
-
B.
Tema
Tema is a biblical figure mentioned in the Old Testament, traditionally regarded as a descendant of Ishmael and associated with a region or tribe in northwestern Arabia.
-
C.
Tema
Tema is a city located within Egypt's Sohag Governorate, known as a regional center in Upper Egypt.
-
D.
TOP
TOP is the IATA airport code for Philip Billard Municipal Airport serving Topeka, Kansas, in the United States.
-
E.
Hot Topics
Hot Topics is the opening discussion segment on the daytime talk show "The View," where the co-hosts debate and comment on current events and trending issues.
- 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_69ca84867bb88190b4b57dd5a56d5691 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9aaaa47881908d69381d4d11f49b |
completed | April 1, 2026, 10:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d17958287081908e337bdbe9ea366f |
completed | April 4, 2026, 8:49 p.m. |
| NEDg | Description generation | batch_69d17d9d69908190879b160968e41745 |
completed | April 4, 2026, 9:07 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d17e4c9e40819081367d2365bf5dd2 |
completed | April 4, 2026, 9:10 p.m. |
Created at: March 30, 2026, 8:09 p.m.