Triple
T3559124
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Theresa |
E75291
|
entity |
| Predicate | variantOf |
P4680
|
FINISHED |
| Object | Teresa |
E348483
|
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: Teresa | Statement: [Theresa, variantOf, Teresa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Teresa Context triple: [Theresa, variantOf, Teresa]
-
A.
Teresa
Teresa is the religious name of Mother Teresa, the Catholic nun and missionary renowned for her charitable work with the poor in Kolkata, India.
-
B.
Teresa
chosen
Teresa is a Mexican telenovela that helped launch Salma Hayek to fame through her lead role as an ambitious, morally conflicted young woman.
-
C.
Teressa
Teressa is a Nicobarese language variety spoken by the indigenous community on Teressa Island in India’s Nicobar archipelago.
-
D.
Santa Teresa Cora
Santa Teresa Cora is a regional dialect of the Cora language spoken by the indigenous Cora people of western Mexico.
-
E.
María
María is a key character in Ernest Hemingway's novel "For Whom the Bell Tolls," known as a young Spanish woman and love interest of the protagonist amid the Spanish Civil War.
- 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_69ad85d45090819086f34fb85d850a1e |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc0881d50819092332491b9527c9d |
completed | March 8, 2026, 6:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3bb9983f48190bda2749d93c74a8d |
completed | March 13, 2026, 7:24 a.m. |
Created at: March 8, 2026, 3:20 p.m.