Triple
T4750042
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gully |
E105454
|
entity |
| Predicate | mainCharacter |
P1183
|
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: [Gully, mainCharacter, Nicky]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nicky Context triple: [Gully, mainCharacter, Nicky]
-
A.
Nicky
chosen
Nicky is a diminutive or nickname commonly used for the given name Nicholas.
-
B.
Nikki
Nikki is a seductive and ambitious burlesque performer featured as one of the central characters in the musical film "Burlesque."
-
C.
Nikki
Nikki is the commonly used first name of American politician and former U.S. Ambassador to the United Nations Nikki Haley.
-
D.
Nicky Barnes
Nicky Barnes was a notorious New York City heroin kingpin and organized crime figure who rose to prominence in the 1970s as the leader of a powerful drug trafficking organization in Harlem.
-
E.
Nicky Katt
Nicky Katt is an American character actor known for his intense, often edgy supporting roles in film and television, including appearances in movies like "Dazed and Confused" and "Sin City."
- 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_69bd43f07fa48190954317d01600994a |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd64c83af48190bd57be79c1505e9d |
completed | March 20, 2026, 3:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be3a561a7c8190a5ab87751ab36e0d |
completed | March 21, 2026, 6:27 a.m. |
Created at: March 20, 2026, 1:20 p.m.