Triple
T11947112
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alanna |
E284326
|
entity |
| Predicate | hasDiminutive |
P456
|
FINISHED |
| Object |
Anna
Anna is a feminine given name widely used across many cultures, often associated with forms of the Hebrew name Hannah meaning "grace" or "favor."
|
E161036
|
NE FINISHED |
How this triple was built (4 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: Anna | Statement: [Alanna, hasDiminutive, Anna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anna Context triple: [Alanna, hasDiminutive, Anna]
-
A.
Anna
Anna is the tragic, aristocratic heroine of Leo Tolstoy’s novel "Anna Karenina," whose passionate affair and struggle against societal norms lead to her downfall.
-
B.
Anna
Anna is a character appearing in the home-renovation reality TV series "Fixer Upper."
-
C.
Anna
Anna of Moscow was a medieval Russian noblewoman and princess associated with the ruling dynasties of Muscovy.
-
D.
Anna
Anna is the popular nickname of C. N. Annadurai, a prominent Indian politician, writer, and founder of the Dravida Munnetra Kazhagam (DMK) party who served as Chief Minister of Tamil Nadu.
-
E.
Anna
Anna is a central fictional character in Michael Ondaatje's novel "Divisadero," around whom much of the story's emotional and narrative complexity revolves.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Anna Triple: [Alanna, hasDiminutive, Anna]
Generated description
Anna is a feminine given name widely used across many cultures, often associated with forms of the Hebrew name Hannah meaning "grace" or "favor."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Anna Target entity description: Anna is a feminine given name widely used across many cultures, often associated with forms of the Hebrew name Hannah meaning "grace" or "favor."
-
A.
Anna
chosen
Anna is a feminine given name of Hebrew origin meaning "grace" or "favor," widely used across many cultures and languages.
-
B.
Anna
Anna is the given first name of Eleanor Roosevelt, the influential former First Lady of the United States and human rights advocate.
-
C.
Anna
Anna is the given first name of Pauli Murray, the pioneering American civil rights activist, lawyer, and Episcopal priest.
-
D.
Anna
Anna is the given name of Anna Laetitia Barbauld, an influential 18th–19th century English poet, essayist, and children's author.
-
E.
Anna
Anna is the given name of Anne Bancroft, the acclaimed American actress best known for her role as Mrs. Robinson in "The Graduate."
- F. None of above.
Provenance (5 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_69d6ab2db38c8190b1f0ed6663ef8ada |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d903456ec0819082b8b10755a6b732 |
completed | April 10, 2026, 2:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f458cbb08881909a71f0592c9231ae |
completed | May 1, 2026, 7:39 a.m. |
| NEDg | Description generation | batch_69f4645a7038819089d7533715f8a430 |
completed | May 1, 2026, 8:29 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f4664ff9608190b23e29b3e5c1c326 |
completed | May 1, 2026, 8:37 a.m. |
Created at: April 8, 2026, 9:45 p.m.