Triple
T8342234
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Therese |
E195946
|
entity |
| Predicate | relatedName |
P3889
|
FINISHED |
| Object | Theresa |
E75291
|
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: Theresa | Statement: [Therese, relatedName, Theresa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Theresa Context triple: [Therese, relatedName, Theresa]
-
A.
Theresa
chosen
Theresa is a feminine given name of Greek origin, commonly associated in modern times with figures such as former UK Prime Minister Theresa May.
-
B.
Teressa
Teressa is a Nicobarese language variety spoken by the indigenous community on Teressa Island in India’s Nicobar archipelago.
-
C.
Juliana
Juliana is an Old English religious poem attributed to the Anglo-Saxon poet Cynewulf, recounting the legend and martyrdom of Saint Juliana.
-
D.
Juliana
Juliana is a feminine given name of Latin origin, commonly used in various European and Latin American countries.
-
E.
Juliana
Juliana was Queen of the Netherlands from 1948 to 1980, known for her down-to-earth style and role in guiding the country through postwar reconstruction and decolonization.
- 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_69ca82ecbdc481908a55cad8ca062d88 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7fe9efec81908e0c9ded3963bac5 |
completed | March 31, 2026, 8:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cdc72bc43c81909d95c7eb6aefc403 |
completed | April 2, 2026, 1:32 a.m. |
Created at: March 30, 2026, 5:58 p.m.