Triple
T14711253
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bella Swan |
E345551
|
entity |
| Predicate | friend |
P8712
|
FINISHED |
| Object |
Angela Weber
Angela Weber is a kind, soft-spoken classmate of Bella Swan in the Twilight series, known for her quiet loyalty and supportive nature.
|
E1117083
|
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: Angela Weber | Statement: [Bella Swan, friend, Angela Weber]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Angela Weber Context triple: [Bella Swan, friend, Angela Weber]
-
A.
Angela Reide
Angela Reide is the central protagonist of "The Division," around whom the story’s key events and character dynamics revolve.
-
B.
Angela Abar
Angela Abar is the masked vigilante Sister Night and central protagonist of the HBO series "Watchmen," navigating themes of race, trauma, and legacy in an alternate-history America.
-
C.
Angela Martin
Angela Martin is a tightly wound, judgmental, and cat-obsessed accountant on the U.S. version of *The Office*, known for her strict moralism and tumultuous office romances.
-
D.
Angela Winkler
Angela Winkler is a German actress acclaimed for her powerful performances in film, television, and theater since the 1970s.
-
E.
Angela Bishop
Angela Bishop is a character in the television series "Dexter: New Blood," where she serves as the Iron Lake police chief.
- 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: Angela Weber Triple: [Bella Swan, friend, Angela Weber]
Generated description
Angela Weber is a kind, soft-spoken classmate of Bella Swan in the Twilight series, known for her quiet loyalty and supportive nature.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Angela Weber Target entity description: Angela Weber is a kind, soft-spoken classmate of Bella Swan in the Twilight series, known for her quiet loyalty and supportive nature.
-
A.
Angela Reide
Angela Reide is the central protagonist of "The Division," around whom the story’s key events and character dynamics revolve.
-
B.
Angela Abar
Angela Abar is the masked vigilante Sister Night and central protagonist of the HBO series "Watchmen," navigating themes of race, trauma, and legacy in an alternate-history America.
-
C.
Angela Martin
Angela Martin is a tightly wound, judgmental, and cat-obsessed accountant on the U.S. version of *The Office*, known for her strict moralism and tumultuous office romances.
-
D.
Angela Winkler
Angela Winkler is a German actress acclaimed for her powerful performances in film, television, and theater since the 1970s.
-
E.
Angela Bishop
Angela Bishop is a character in the television series "Dexter: New Blood," where she serves as the Iron Lake police chief.
- 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_69deb9814e0c8190984ac30d276499cc |
completed | April 14, 2026, 10:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdfb845de08190b933d90809cde830 |
completed | May 8, 2026, 3:04 p.m. |
| NEDg | Description generation | batch_69fdffa3b03c819094692ec99e48c851 |
completed | May 8, 2026, 3:22 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fdfff147988190800d74b49bf6adf1 |
completed | May 8, 2026, 3:23 p.m. |
Created at: April 10, 2026, 1:28 a.m.