Triple
T6812497
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nicola |
E156668
|
entity |
| Predicate | hasDiminutive |
P456
|
FINISHED |
| Object | Nicky |
E157581
|
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: Nicky | Statement: [Nicola, hasDiminutive, Nicky]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nicky Context triple: [Nicola, hasDiminutive, Nicky]
-
A.
Nicky
chosen
Nicky is a diminutive or nickname commonly used for the given name Nicholas.
-
B.
Nikki
Nikki is the central protagonist of the 1993 coming-of-age sports comedy film "Airborne," known for his laid-back California surfer attitude and exceptional inline skating skills.
-
C.
Nikki
Nikki is a seductive and ambitious burlesque performer featured as one of the central characters in the musical film "Burlesque."
-
D.
Nikki
Nikki is the estranged wife of Pat Solitano in the film "Silver Linings Playbook," whose separation from him drives much of the movie’s emotional conflict.
-
E.
Nikki
Nikki is the commonly used first name of American politician and former U.S. Ambassador to the United Nations Nikki Haley.
- 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_69c68828b26c819090fe9df7612bbc27 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d329861881909f65bd1017ea384b |
completed | March 27, 2026, 6:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c723d775a48190bfdf5b6a52339833 |
completed | March 28, 2026, 12:41 a.m. |
Created at: March 27, 2026, 2:17 p.m.