Triple
T12258559
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Soco |
E292162
|
entity |
| Predicate | performer |
P1363
|
FINISHED |
| Object | Terri |
unclear NED1
|
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: Terri | Statement: [Soco, performer, Terri]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Terri Context triple: [Soco, performer, Terri]
-
A.
Terri
Terri is a supporting character in the comedy film "Beauty Shop," contributing to the ensemble of stylists and clients at the salon.
-
B.
Terri
Terri is a common diminutive form of the given name Theresa.
-
C.
Teressa
Teressa is a Nicobarese language variety spoken by the indigenous community on Teressa Island in India’s Nicobar archipelago.
-
D.
Teri
Teri is a central character in the film and television series "Soul Food," known as the ambitious, high-powered attorney whose strained relationships with her family drive much of the story’s drama.
-
E.
Trisha
Trisha is a prominent Indian actress best known for her leading roles in Tamil films and her significant impact on South Indian cinema.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
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_69d6ab67950c8190be08450a06228c4b |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91ccadc3c81908fe68adc3fdcc851 |
completed | April 10, 2026, 3:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f61e63da6081908840b1e37fd39b88 |
completed | May 2, 2026, 3:55 p.m. |
Created at: April 8, 2026, 9:52 p.m.