Triple
T12870187
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | High Seas |
E307826
|
entity |
| Predicate | hasMainCharacter |
P1183
|
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: [High Seas, hasMainCharacter, Teresa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Teresa Context triple: [High Seas, hasMainCharacter, Teresa]
-
A.
Teresa
Teresa is the middle name of Tamar Teresa Day Hennessy.
-
B.
Teresa
"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 central protagonist of the play "The Memory of Water," around whom the story’s emotional and familial conflicts revolve.
-
D.
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.
-
E.
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.
- 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_69d7bdf69bc48190af6c2621f28ca351 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d970905784819091631161a9de98c5 |
completed | April 10, 2026, 9:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6a55161a881908d767653c17d3acc |
completed | May 3, 2026, 1:30 a.m. |
Created at: April 9, 2026, 5:38 p.m.