Triple
T19868211
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Legally Blonde |
E477445
|
entity |
| Predicate | authorOfSourceWork |
P2353
|
FINISHED |
| Object |
Amanda Brown
Amanda Brown is an American author best known for writing the novel that inspired the hit film "Legally Blonde."
|
E1458882
|
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: Amanda Brown | Statement: [Legally Blonde, authorOfSourceWork, Amanda Brown]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amanda Brown Context triple: [Legally Blonde, authorOfSourceWork, Amanda Brown]
-
A.
Amanda Brown
Amanda Brown is an Australian musician best known as the multi-instrumentalist and violinist for the indie rock band The Go-Betweens.
-
B.
Amanda Robinson
Amanda Robinson is the spouse of Jason Robinson.
-
C.
Amanda Young
Amanda Young is a central character in the Saw horror film franchise, known for being one of Jigsaw’s most prominent protégés and later a conflicted antagonist.
-
D.
Amanda Clayton
Amanda Clayton is an American actress best known for her role in the crime drama television series "City on a Hill."
-
E.
Amanda Woods
Amanda Woods is a successful but emotionally guarded Los Angeles movie trailer producer who swaps homes with a British woman over Christmas in the romantic comedy film "The Holiday."
- 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: Amanda Brown Triple: [Legally Blonde, authorOfSourceWork, Amanda Brown]
Generated description
Amanda Brown is an American author best known for writing the novel that inspired the hit film "Legally Blonde."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Amanda Brown Target entity description: Amanda Brown is an American author best known for writing the novel that inspired the hit film "Legally Blonde."
-
A.
Amanda Brown
Amanda Brown is an Australian musician best known as the multi-instrumentalist and violinist for the indie rock band The Go-Betweens.
-
B.
Amanda Robinson
Amanda Robinson is the spouse of Jason Robinson.
-
C.
Amanda Young
Amanda Young is a central character in the Saw horror film franchise, known for being one of Jigsaw’s most prominent protégés and later a conflicted antagonist.
-
D.
Amanda Clayton
Amanda Clayton is an American actress best known for her role in the crime drama television series "City on a Hill."
-
E.
Amanda Woods
Amanda Woods is a successful but emotionally guarded Los Angeles movie trailer producer who swaps homes with a British woman over Christmas in the romantic comedy film "The Holiday."
- 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_69d8e51e7d948190aedbcd6c30361c39 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e658a168288190a2fbb735d1fd30a8 |
completed | April 20, 2026, 4:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09276e98588190ad3bdb9d04a19584 |
completed | May 17, 2026, 2:26 a.m. |
| NEDg | Description generation | batch_6a0928a53e3c8190a8e636f5f1387f73 |
completed | May 17, 2026, 2:32 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0929485ebc8190ab8bc316b3e802ca |
completed | May 17, 2026, 2:34 a.m. |
Created at: April 10, 2026, 1:51 p.m.