Triple
T17472890
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Princess Theodora of Greece and Denmark |
E425462
|
entity |
| Predicate | engagedTo |
P17846
|
FINISHED |
| Object |
Matthew Kumar
Matthew Kumar is a British-Indian lawyer known publicly as the fiancé of Princess Theodora of Greece and Denmark.
|
E1270251
|
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: Matthew Kumar | Statement: [Princess Theodora of Greece and Denmark, engagedTo, Matthew Kumar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matthew Kumar Context triple: [Princess Theodora of Greece and Denmark, engagedTo, Matthew Kumar]
-
A.
Vikram Kumar
Vikram Kumar is an Indian film director and screenwriter known for his work in Tamil and Telugu cinema, including acclaimed films like "24" and "Manam."
-
B.
Kumar Saurabh
Kumar Saurabh is a technology entrepreneur best known as a co-founder of the cloud-based machine data analytics company Sumo Logic.
-
C.
Gautam Kumar
Gautam Kumar is known as the son of legendary Indian Bengali actor Uttam Kumar.
-
D.
Kunal Khemu
Kunal Khemu is an Indian film actor who began his career as a popular child artist in the 1990s and later gained recognition for his roles in Hindi comedies and dramas.
-
E.
Suraj Sharma
Suraj Sharma is an Indian actor best known for his breakout performance as the shipwrecked teenager Pi Patel in Ang Lee’s acclaimed film "Life of Pi."
- 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: Matthew Kumar Triple: [Princess Theodora of Greece and Denmark, engagedTo, Matthew Kumar]
Generated description
Matthew Kumar is a British-Indian lawyer known publicly as the fiancé of Princess Theodora of Greece and Denmark.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Matthew Kumar Target entity description: Matthew Kumar is a British-Indian lawyer known publicly as the fiancé of Princess Theodora of Greece and Denmark.
-
A.
Vikram Kumar
Vikram Kumar is an Indian film director and screenwriter known for his work in Tamil and Telugu cinema, including acclaimed films like "24" and "Manam."
-
B.
Kumar Saurabh
Kumar Saurabh is a technology entrepreneur best known as a co-founder of the cloud-based machine data analytics company Sumo Logic.
-
C.
Gautam Kumar
Gautam Kumar is known as the son of legendary Indian Bengali actor Uttam Kumar.
-
D.
Kunal Khemu
Kunal Khemu is an Indian film actor who began his career as a popular child artist in the 1990s and later gained recognition for his roles in Hindi comedies and dramas.
-
E.
Suraj Sharma
Suraj Sharma is an Indian actor best known for his breakout performance as the shipwrecked teenager Pi Patel in Ang Lee’s acclaimed film "Life of Pi."
- 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_69d889dbc2e88190b18ea6115e819258 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e451b990848190b2e8510d67e94b79 |
completed | April 19, 2026, 3:53 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01b82b4bc08190b293c18f49909cb0 |
completed | May 11, 2026, 11:06 a.m. |
| NEDg | Description generation | batch_6a01b8b8785081909b154331f92e2957 |
completed | May 11, 2026, 11:08 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a01b9343028819082933b51bb092b3d |
completed | May 11, 2026, 11:10 a.m. |
Created at: April 10, 2026, 5:47 a.m.