Triple
T10028006
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carmen Dillon |
E204779
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Carmen
Carmen is a feminine given name of Spanish origin, famously associated with the heroine of Georges Bizet’s opera.
|
E358979
|
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: Carmen | Statement: [Carmen Dillon, givenName, Carmen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Carmen Context triple: [Carmen Dillon, givenName, Carmen]
-
A.
Carmen
Carmen is a key character in the dark fantasy film "Pan’s Labyrinth," serving as the pregnant mother whose fragile health and marriage to a brutal captain frame the story’s wartime and familial tensions.
-
B.
Carmen
Carmen is a central district of San José, Costa Rica, known for its urban character and role in the capital’s administrative and commercial life.
-
C.
Carmen
Carmen is a supporting character in Jim Jarmusch’s film "Broken Flowers," connected to the protagonist’s journey to revisit women from his past.
-
D.
Carmen
Carmen is a landlocked municipality in the central part of Bohol Island in the Philippines, known for its proximity to the famous Chocolate Hills.
-
E.
Carmen
Carmen is a pivotal character in the 1986 film "The Color of Money," serving as the savvy and manipulative girlfriend-manager of young pool hustler Vincent Lauria.
- 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: Carmen Triple: [Carmen Dillon, givenName, Carmen]
Generated description
Carmen is a feminine given name of Spanish origin, famously associated with the heroine of Georges Bizet’s opera.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Carmen Target entity description: Carmen is a feminine given name of Spanish origin, famously associated with the heroine of Georges Bizet’s opera.
-
A.
Carmen
chosen
Carmen is a feminine given name of Latin origin, widely used in Spanish-speaking cultures and beyond.
-
B.
Carmen
Carmen is a famous opera by Georges Bizet, renowned for its passionate music and tragic story centered on the free-spirited gypsy Carmen.
-
C.
Carmen
Carmen is a 1983 Spanish musical drama film directed by Carlos Saura that reimagines the classic Bizet opera through flamenco dance.
-
D.
Carmen
Carmen is a central district of San José, Costa Rica, known for its urban character and role in the capital’s administrative and commercial life.
-
E.
Carmen
Carmen is a municipality in the province of Cebu in the Philippines, known for its agricultural economy and proximity to coastal and upland attractions.
- F. None of above.
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_69ca834d77188190ad645e33e8ca3200 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cdcde51c408190afb34010b1707014 |
completed | April 2, 2026, 2:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2822bca308190ad2fad82653c6e74 |
completed | April 5, 2026, 3:39 p.m. |
| NEDg | Description generation | batch_69d2861c687881908dc40ac31d7cda93 |
completed | April 5, 2026, 3:56 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d286a0eb34819093b6ba03df28b271 |
completed | April 5, 2026, 3:58 p.m. |
Created at: March 30, 2026, 8:54 p.m.