Triple
T14655609
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Broken Embraces |
E344099
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object |
Tamar Novas
Tamar Novas is a Spanish actor known for his work in film and television, including collaborations with acclaimed director Pedro Almodóvar.
|
E1112926
|
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: Tamar Novas | Statement: [Broken Embraces, starring, Tamar Novas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tamar Novas Context triple: [Broken Embraces, starring, Tamar Novas]
-
A.
Tamar Thomas
Tamar Thomas is a film producer known for her work on the critically acclaimed 2021 drama "Belfast," directed by Kenneth Branagh.
-
B.
Tamara
Tamara is a feminine given name of Hebrew origin, commonly used in various cultures and languages.
-
C.
Tita Merello
Tita Merello was a renowned Argentine actress, tango singer, and cultural icon whose career spanned film, theater, and music from the 1930s onward.
-
D.
Amada Cruz
Amada Cruz is an American museum director and arts administrator known for leading major art institutions, including serving as director of the Seattle Art Museum.
-
E.
Nina Romina
Nina Romina is a ruthless local TV news director in the film "Nightcrawler," known for her willingness to exploit violent crime footage to boost ratings.
- 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: Tamar Novas Triple: [Broken Embraces, starring, Tamar Novas]
Generated description
Tamar Novas is a Spanish actor known for his work in film and television, including collaborations with acclaimed director Pedro Almodóvar.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tamar Novas Target entity description: Tamar Novas is a Spanish actor known for his work in film and television, including collaborations with acclaimed director Pedro Almodóvar.
-
A.
Tamar Thomas
Tamar Thomas is a film producer known for her work on the critically acclaimed 2021 drama "Belfast," directed by Kenneth Branagh.
-
B.
Tamara
Tamara is a feminine given name of Hebrew origin, commonly used in various cultures and languages.
-
C.
Tita Merello
Tita Merello was a renowned Argentine actress, tango singer, and cultural icon whose career spanned film, theater, and music from the 1930s onward.
-
D.
Amada Cruz
Amada Cruz is an American museum director and arts administrator known for leading major art institutions, including serving as director of the Seattle Art Museum.
-
E.
Nina Romina
Nina Romina is a ruthless local TV news director in the film "Nightcrawler," known for her willingness to exploit violent crime footage to boost ratings.
- 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_69d822e1a2cc81908e5bb93cf61ce3cc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb51a562c819098971447db4b29f7 |
completed | April 14, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdd5de0b98819094c32765e4cb3f9c |
completed | May 8, 2026, 12:23 p.m. |
| NEDg | Description generation | batch_69fddd8d7da481909d38d9390770939c |
completed | May 8, 2026, 12:56 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fdde23da708190b7eabeed6a9cb169 |
completed | May 8, 2026, 12:59 p.m. |
Created at: April 10, 2026, 1:27 a.m.