Triple
T6807588
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Diane |
E156346
|
entity |
| Predicate | shortForm |
P43
|
FINISHED |
| Object | Di |
E356031
|
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: Di | Statement: [Diane, shortForm, Di]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Di Context triple: [Diane, shortForm, Di]
-
A.
Di
chosen
Di is a common shortened form of the given name Diana, often used as an affectionate nickname.
-
B.
DI
DI is the abbreviation for Defence Intelligence, the United Kingdom’s military intelligence organization responsible for providing strategic and operational intelligence to the government and armed forces.
-
C.
DiDi
DiDi is a major Chinese ride-hailing and mobility technology company that operates a platform for on-demand transportation and related services.
-
D.
DiDi
DiDi is an American professional basketball player known for her defensive prowess and collegiate success with the Baylor Lady Bears.
-
E.
It
It is a 1986 horror novel by Stephen King about a shape-shifting entity that terrorizes children in the town of Derry, Maine.
- 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_69c68826e6a48190a3d220b541e639de |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d30a006081908996e31aa7ced0ac |
completed | March 27, 2026, 6:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c71aa27cec81909f45911ffa44ea6f |
completed | March 28, 2026, 12:02 a.m. |
Created at: March 27, 2026, 2:16 p.m.