Triple
T12320066
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tyler Summitt |
E293703
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Tyler
Tyler is a masculine given name of English origin that is commonly used in the United States and other English-speaking countries.
|
E674057
|
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: Tyler | Statement: [Tyler Summitt, givenName, Tyler]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tyler Context triple: [Tyler Summitt, givenName, Tyler]
-
A.
John
John is the first name of Johnny Evers, a Hall of Fame American Major League Baseball second baseman who starred for the Chicago Cubs in the early 20th century.
-
B.
John
John is the middle name of Samuel John Mills, an American Congregationalist minister known for his role in early 19th-century missionary movements.
-
C.
John
John is the given name of John Eales, the renowned former Australian rugby union captain and World Cup winner.
-
D.
John
John is the common given name of American author and YouTube creator John Green, known for novels like "The Fault in Our Stars" and for co-founding the Vlogbrothers channel.
-
E.
John
John McDowell is a prominent South African-born philosopher known for his influential work in epistemology, philosophy of mind, and ethics.
- 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: Tyler Triple: [Tyler Summitt, givenName, Tyler]
Generated description
Tyler is a masculine given name of English origin that is commonly used in the United States and other English-speaking countries.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tyler Target entity description: Tyler is a masculine given name of English origin that is commonly used in the United States and other English-speaking countries.
-
A.
Tyler
chosen
Tyler is a masculine given name commonly used in English-speaking countries, originally derived from an occupational surname meaning "tile maker" or "house builder."
-
B.
Tyler
Tyler is a surname most prominently associated with American actress Liv Tyler and various other notable figures in entertainment and public life.
-
C.
Tyler
Tyler is a mid-sized city in East Texas known for its rose cultivation, annual Texas Rose Festival, and role as a regional medical and educational hub.
-
D.
Tyler
Tyler is the officer in a Masonic lodge responsible for guarding the entrance and ensuring only qualified individuals are admitted to meetings.
-
E.
Tyler
Tyler is a fictional character appearing in the American television series "Kristin."
- 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_69d6ab6ae0dc8190b1522a9c1c55c114 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f4c2b548190938fff9427f07dc7 |
completed | April 10, 2026, 6:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f61e8aa94881908e4c184062037ab5 |
completed | May 2, 2026, 3:55 p.m. |
| NEDg | Description generation | batch_69f61f5e20cc8190a84f50ddded76974 |
completed | May 2, 2026, 3:59 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f62045b20c819083c755fbe99a9a7f |
completed | May 2, 2026, 4:03 p.m. |
Created at: April 8, 2026, 9:53 p.m.