Triple
T8030663
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hannah Fry |
E186969
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Hannah |
E112936
|
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: Hannah | Statement: [Hannah Fry, givenName, Hannah]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hannah Context triple: [Hannah Fry, givenName, Hannah]
-
A.
Hannah
chosen
Hannah is a biblical figure in the Book of 1 Samuel known for her fervent prayer for a child and as the mother of the prophet Samuel.
-
B.
Hannah
Hannah is a person associated in some way with the city of Santa Ana, California.
-
C.
Hannah
Hannah is a key survivor character in the British post-apocalyptic horror film "28 Days Later," known for her resilience and resourcefulness amid a rage virus outbreak in London.
-
D.
Hannah
Hannah is an alternate given name associated with American actress Dakota Fanning, whose full name is Hannah Dakota Fanning.
-
E.
Hannah
Hannah is an alternative name or alias for the individual known as Anne.
- 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_69ca82ad4e2c8190a693e3c9e30fe66f |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3eef921081908d0ea21f142c175a |
completed | March 31, 2026, 3:26 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc56e812dc81908916fc7163ae344a |
completed | March 31, 2026, 11:21 p.m. |
Created at: March 30, 2026, 5:22 p.m.