Triple
T14711964
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Cape |
E345566
|
entity |
| Predicate | productionCompany |
P490
|
FINISHED |
| Object |
BermanBraun
BermanBraun is a media production company known for creating television, film, and digital content across a variety of genres.
|
E1116246
|
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: BermanBraun | Statement: [The Cape, productionCompany, BermanBraun]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: BermanBraun Context triple: [The Cape, productionCompany, BermanBraun]
-
A.
Braun
Braun is a German surname most infamously associated with Eva Braun, the longtime companion and brief wife of Adolf Hitler.
-
B.
Berman
Berman is a surname of Ashkenazi Jewish origin borne by various notable individuals across fields such as art, politics, and academia.
-
C.
Schwarzkopf
Schwarzkopf is a German surname most prominently associated with U.S. Army General Norman Schwarzkopf Jr., who led coalition forces in the Gulf War.
-
D.
Schwarzkopf
Schwarzkopf is a German hair care and styling brand known for its shampoos, dyes, and professional salon products.
-
E.
Bumble and bumble
Bumble and bumble is a professional haircare and hairstyling brand known for its salon-quality products and trend-setting approach to hair fashion.
- 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: BermanBraun Triple: [The Cape, productionCompany, BermanBraun]
Generated description
BermanBraun is a media production company known for creating television, film, and digital content across a variety of genres.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: BermanBraun Target entity description: BermanBraun is a media production company known for creating television, film, and digital content across a variety of genres.
-
A.
Braun
Braun is a German surname most infamously associated with Eva Braun, the longtime companion and brief wife of Adolf Hitler.
-
B.
Berman
Berman is a surname of Ashkenazi Jewish origin borne by various notable individuals across fields such as art, politics, and academia.
-
C.
Schwarzkopf
Schwarzkopf is a German surname most prominently associated with U.S. Army General Norman Schwarzkopf Jr., who led coalition forces in the Gulf War.
-
D.
Schwarzkopf
Schwarzkopf is a German hair care and styling brand known for its shampoos, dyes, and professional salon products.
-
E.
Bumble and bumble
Bumble and bumble is a professional haircare and hairstyling brand known for its salon-quality products and trend-setting approach to hair fashion.
- 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_69d822e4a8c08190a155df736bb7bc13 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb982bf248190881e21a8a0861a3f |
completed | April 14, 2026, 10:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdf08f2aa08190a5ac3240d1de90fb |
completed | May 8, 2026, 2:17 p.m. |
| NEDg | Description generation | batch_69fdf23d4928819093630e25616abb2d |
completed | May 8, 2026, 2:25 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fdf31fcb4081908a88cf4d4c5ddced |
completed | May 8, 2026, 2:28 p.m. |
Created at: April 10, 2026, 1:28 a.m.