Triple
T17075101
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hilary Tsui |
E414327
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Hilary |
E335389
|
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: Hilary | Statement: [Hilary Tsui, givenName, Hilary]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hilary Context triple: [Hilary Tsui, givenName, Hilary]
-
A.
Hilary
chosen
Hilary is a given name most notably borne by the influential American philosopher Hilary Putnam.
-
B.
Hilary
Hilary is a central character in Debbie Isitt’s darkly comic play "The Woman Who Cooked Her Husband," which explores themes of marital betrayal and revenge.
-
C.
Hillary
Hillary is a surname most prominently associated with New Zealand mountaineer Sir Edmund Hillary and his family, including his son Peter Hillary.
-
D.
Hillary Clinton
Hillary Clinton is an American politician and diplomat who served as U.S. Secretary of State, U.S. senator from New York, First Lady, and the first woman to be a major party’s presidential nominee.
-
E.
Michelle
Michelle is the resourceful and determined protagonist of the psychological thriller film "10 Cloverfield Lane."
- 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_69d886cef44c8190ba56c44b4e863e64 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dbc47808819088a4ca039689b213 |
completed | April 18, 2026, 7:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0139f5d34081908bd9e88fb5772ddc |
completed | May 11, 2026, 2:07 a.m. |
Created at: April 10, 2026, 5:34 a.m.