Triple
T3662476
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Henrietta |
E77681
|
entity |
| Predicate | hasDiminutive |
P456
|
FINISHED |
| Object | Hettie |
E225006
|
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: Hettie | Statement: [Henrietta, hasDiminutive, Hettie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hettie Context triple: [Henrietta, hasDiminutive, Hettie]
-
A.
Sadie Smith
Sadie Smith is the birth name of Zadie Smith, the acclaimed British novelist known for works such as "White Teeth" and "On Beauty."
-
B.
Hazel Bennet
Hazel Bennet was the wife of American film director and actor Lloyd Bacon, associated with Hollywood’s early studio era.
-
C.
Margaret
Margaret is a feminine given name of Greek origin, traditionally associated with the meaning "pearl" and widely used in English-speaking countries.
-
D.
Margaret
Margaret is a 2011 American drama film written and directed by Kenneth Lonergan, known for its complex portrayal of grief and moral responsibility following a tragic bus accident in New York City.
-
E.
Ethel
chosen
Ethel is a feminine given name of Old English origin, historically popular in English-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_69ad85dfc4dc8190a441864202ab2a7a |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc3fcd910819082012b10b23860aa |
completed | March 8, 2026, 6:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b48846af9881909d71d63b8bd8d141 |
completed | March 13, 2026, 9:57 p.m. |
Created at: March 8, 2026, 3:25 p.m.