Triple
T2587336
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Patricia |
E58034
|
entity |
| Predicate | shortForm |
P43
|
FINISHED |
| Object | Tisha |
E278784
|
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: Tisha | Statement: [Patricia, shortForm, Tisha]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tisha Context triple: [Patricia, shortForm, Tisha]
-
A.
Tisha
chosen
Tisha is a feminine given name, often used as a diminutive or variant of names like Patricia or Letitia.
-
B.
Shira
Shira is the eroded western volcanic cone and plateau of Mount Kilimanjaro, forming one of the mountain’s three main summits.
-
C.
Kirsha
Kirsha is a central character in Naguib Mahfouz’s novel "Midaq Alley," known as the café owner whose personal life and hidden desires reflect the social and moral tensions of mid-20th-century Cairo.
-
D.
Shosha
Shosha is a novel by Nobel Prize–winning author Isaac Bashevis Singer that portrays a doomed love story set against the backdrop of pre–World War II Jewish Warsaw.
-
E.
Shimsha
Shimsha is a river in southern India that flows through Karnataka and is known for its waterfalls and contribution to the Kaveri river system.
- 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_69af83ba83c48190bf65ff666830c758 |
completed | March 10, 2026, 2:36 a.m. |
Created at: March 6, 2026, 9:49 p.m.