Triple
T15413641
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mia X |
E369154
|
entity |
| Predicate | birthName |
P65
|
FINISHED |
| Object |
Mia Young
Mia Young is an individual whose given name is used as her professional and personal identity.
|
E1158926
|
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: Mia Young | Statement: [Mia X, birthName, Mia Young]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mia Young Context triple: [Mia X, birthName, Mia Young]
-
A.
Sophia Young
Sophia Young is a retired WNBA forward best known as a multi-time All-Star and franchise cornerstone of the San Antonio Silver Stars.
-
B.
Mia Dolan
Mia Dolan is an aspiring actress in Los Angeles and one of the two central protagonists of the musical film "La La Land."
-
C.
Mia Morgan
Mia Morgan is a central character in the romantic comedy-drama film "The Best Man," around whom much of the story’s interpersonal conflict and emotional tension revolves.
-
D.
Hanna Young
Hanna Young is a central character in the television drama "The Haves and the Have Nots," known for her role as a hardworking, morally grounded single mother navigating the stark divides of wealth and power.
-
E.
Mia Brooks
Mia Brooks is a main character in the teen drama series "Love, Victor," known as Victor's intelligent, artistic, and compassionate friend and love interest.
- 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: Mia Young Triple: [Mia X, birthName, Mia Young]
Generated description
Mia Young is an individual whose given name is used as her professional and personal identity.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mia Young Target entity description: Mia Young is an individual whose given name is used as her professional and personal identity.
-
A.
Sophia Young
Sophia Young is a retired WNBA forward best known as a multi-time All-Star and franchise cornerstone of the San Antonio Silver Stars.
-
B.
Mia Dolan
Mia Dolan is an aspiring actress in Los Angeles and one of the two central protagonists of the musical film "La La Land."
-
C.
Mia Morgan
Mia Morgan is a central character in the romantic comedy-drama film "The Best Man," around whom much of the story’s interpersonal conflict and emotional tension revolves.
-
D.
Hanna Young
Hanna Young is a central character in the television drama "The Haves and the Have Nots," known for her role as a hardworking, morally grounded single mother navigating the stark divides of wealth and power.
-
E.
Mia Brooks
Mia Brooks is a main character in the teen drama series "Love, Victor," known as Victor's intelligent, artistic, and compassionate friend and love interest.
- 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_69d85a1849f48190bf898068b2806fae |
completed | April 10, 2026, 2:02 a.m. |
| NER | Named-entity recognition | batch_69e03ea7561481909b04e613e2352f82 |
completed | April 16, 2026, 1:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff2cf0e90c8190a339e6fca53a02d8 |
completed | May 9, 2026, 12:47 p.m. |
| NEDg | Description generation | batch_69ff2e1fb27c81908de0d755bf30c833 |
completed | May 9, 2026, 12:52 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff2f3ab6988190b4cefe2f55c4101c |
completed | May 9, 2026, 12:57 p.m. |
Created at: April 10, 2026, 3:20 a.m.