Triple
T9709703
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stewart Stern |
E234990
|
entity |
| Predicate | wroteScreenplayFor |
P15305
|
FINISHED |
| Object | Teresa |
E816774
|
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: [Stewart Stern, wroteScreenplayFor, Teresa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Teresa Context triple: [Stewart Stern, wroteScreenplayFor, Teresa]
-
A.
Teresa
Teresa is the middle name of Tamar Teresa Day Hennessy.
-
B.
Teresa
chosen
"Teresa" is a film project associated with screenwriter Stewart Stern, best known for his work on "Rebel Without a Cause."
-
C.
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.
-
D.
Teresa
Teresa is a central figure in Carlos Fuentes’s novel "The Death of Artemio Cruz," representing both a pivotal love interest and a symbol of the social and emotional conflicts surrounding the protagonist.
-
E.
Teresa
Teresa is a municipality in the province of Rizal in the Philippines, known for its residential communities and proximity to Metro Manila.
- 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_69ca84cd8fa0819090a5e243ceb37003 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9da7c6188190b086f7e411378268 |
completed | April 1, 2026, 10:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1af93b76c81908377f17956fb86b1 |
completed | April 5, 2026, 12:40 a.m. |
Created at: March 30, 2026, 8:19 p.m.