Triple
T8342249
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Therese |
E195946
|
entity |
| Predicate | variantSpelling |
P457
|
FINISHED |
| Object | Therése |
E195946
|
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: Therése | Statement: [Therese, variantSpelling, Therése]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Therése Context triple: [Therese, variantSpelling, Therése]
-
A.
Therese
chosen
Therese is a feminine given name of French origin, commonly associated with Christian saints and used in various European cultures.
-
B.
Teressa
Teressa is a Nicobarese language variety spoken by the indigenous community on Teressa Island in India’s Nicobar archipelago.
-
C.
Madelaine
Madelaine is a character in the Danish crime thriller film "The Salvation."
-
D.
Teresa
Teresa is the middle name of Tamar Teresa Day Hennessy.
-
E.
Teresa
Teresa is a feminine given name commonly used in various cultures, often associated with notable religious and historical figures.
- 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_69ce1cf20900819084ab766192f36239 |
completed | April 2, 2026, 7:38 a.m. |
Created at: March 30, 2026, 5:58 p.m.