Triple
T12821799
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sophia Chew Nicklin Dallas |
E306547
|
entity |
| Predicate | hasGivenName |
P17
|
FINISHED |
| Object | Sophia |
E306547
|
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: Sophia | Statement: [Sophia Chew Nicklin Dallas, hasGivenName, Sophia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sophia Context triple: [Sophia Chew Nicklin Dallas, hasGivenName, Sophia]
-
A.
Sophia
"Sophia" is a lesser-known literary work by British novelist Anthony Hope, best known for his adventure classic "The Prisoner of Zenda."
-
B.
Sophia
chosen
Sophia is a person whose given name is used in the full name Sophia Chew Nicklin Dallas.
-
C.
Sophia
Sophia was a prominent Byzantine empress of the Justinian dynasty, known for her political influence and role in imperial court affairs during the 6th century.
-
D.
Sophia
Sophia is the young, unhappily married woman at the center of the historical romance and art-themed drama in "Tulip Fever."
-
E.
Sophia
Sophia is the birth name of American actress Sylvia Sidney, a prominent film and stage performer of the 1930s and later character roles.
- 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_69d7bdf46c448190b1faa55aaacb6317 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96e9fcc8c8190a926ab0481d28f14 |
completed | April 10, 2026, 9:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f68ed165188190a4cac781c753fb23 |
completed | May 2, 2026, 11:54 p.m. |
Created at: April 9, 2026, 5:32 p.m.