Triple
T12520366
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daniel Tyler |
E299297
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Tyler
Tyler is a common English surname of Old French origin that historically referred to someone who made or laid tiles.
|
E243190
|
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: [Daniel Tyler, familyName, Tyler]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tyler Context triple: [Daniel Tyler, familyName, Tyler]
-
A.
John
John is the birth name of American singer-songwriter and producer Teddy Geiger, known for writing and producing hits for artists like Shawn Mendes.
-
B.
John
John I, Count of Holland, was a medieval nobleman who ruled the County of Holland at the turn of the 14th century.
-
C.
John
John McDowell is a prominent South African-born philosopher known for his influential work in epistemology, philosophy of mind, and ethics.
-
D.
John
John Cicero was a late 15th-century Elector of Brandenburg from the House of Hohenzollern who helped consolidate the territory’s political and administrative structures within the Holy Roman Empire.
-
E.
John
John Brabourne was a British film and television producer and peer, known for producing works such as the 1979 adaptation of "Murder on the Orient Express."
- 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: [Daniel Tyler, familyName, Tyler]
Generated description
Tyler is a common English surname of Old French origin that historically referred to someone who made or laid tiles.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tyler Target entity description: Tyler is a common English surname of Old French origin that historically referred to someone who made or laid tiles.
-
A.
Tyler
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
chosen
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 the officer in a Masonic lodge responsible for guarding the entrance and ensuring only qualified individuals are admitted to meetings.
-
D.
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.
-
E.
Tyler
Tyler is a character in the 2015 horror-thriller film "The Visit," serving as one of the two grandchildren whose unsettling stay with their grandparents drives the movie’s plot.
- 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_69d6ada5cdd48190860d9ce30aff69be |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d9545b2b2481909049a490c97678f2 |
completed | April 10, 2026, 7:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f64bbf43e08190ae79f92ed5882ce2 |
completed | May 2, 2026, 7:08 p.m. |
| NEDg | Description generation | batch_69f64c7c5d04819094fcbee0a4b5cbb4 |
completed | May 2, 2026, 7:11 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f64d668e548190979b3da72fe21ae7 |
completed | May 2, 2026, 7:15 p.m. |
Created at: April 8, 2026, 9:57 p.m.