Triple
T10225405
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anne Tyler |
E243190
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Tyler |
E243190
|
NE FINISHED |
How this triple was built (2 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: [Anne Tyler, familyName, Tyler]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tyler Context triple: [Anne Tyler, familyName, Tyler]
-
A.
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.
-
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 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.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d381b0f97c819085c9b45799a5fb7c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d1f9cf6c81909a6b9e9b9d0a79fe |
completed | April 7, 2026, 9:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d7fb26d17c8190848b6b0d4df06fa2 |
completed | April 9, 2026, 7:16 p.m. |
Created at: April 6, 2026, 11:17 a.m.