Triple
T2587334
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Patricia |
E58034
|
entity |
| Predicate | shortForm |
P43
|
FINISHED |
| Object | Tricia |
E278783
|
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: Tricia | Statement: [Patricia, shortForm, Tricia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tricia Context triple: [Patricia, shortForm, Tricia]
-
A.
Tricia
chosen
Tricia is a feminine given name commonly used as a shortened or informal form of Patricia.
-
B.
Trisha
Trisha is a prominent Indian actress best known for her leading roles in Tamil films and her significant impact on South Indian cinema.
-
C.
Tracy
Tracy is a city in California’s Central Valley known as a growing suburban community within the San Francisco Bay Area’s commuter belt.
-
D.
Tracy
Tracy is a given name commonly used for both males and females in English-speaking countries.
-
E.
Adrienne
Adrienne is a feminine given name of French origin, commonly used in English- and French-speaking countries.
- 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_69ab4ac019c8819094add11c46706e32 |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abd3f8a3888190889c537e6df07305 |
completed | March 7, 2026, 7:30 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af907510d481909e7c5e5207d17774 |
completed | March 10, 2026, 3:31 a.m. |
Created at: March 6, 2026, 9:49 p.m.