Triple
T6860583
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nick Collison |
E158266
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Nicholas |
E28979
|
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: Nicholas | Statement: [Nick Collison, givenName, Nicholas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nicholas Context triple: [Nick Collison, givenName, Nicholas]
-
A.
Nicholas
chosen
Nicholas is a masculine given name of Greek origin, commonly used in many cultures and historically borne by numerous saints, rulers, and notable figures.
-
B.
Nicholas Herrick
Nicholas Herrick was the father of the 17th-century English lyric poet and cleric Robert Herrick.
-
C.
Rupert
Rupert is a masculine given name of Germanic origin, commonly used in English-speaking countries and borne by various notable figures.
-
D.
Rupert
Rupert is a small town located in Greenbrier County in the state of West Virginia, United States.
-
E.
Nicholas Van Orton
Nicholas Van Orton is a wealthy, emotionally detached investment banker whose life unravels after he becomes entangled in a mysterious and elaborate psychological "game" in the film *The Game*.
- 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_69c68830cdbc8190a8301c7a9d9f651a |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d88659c8819084916663219a8198 |
completed | March 27, 2026, 7:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c72f9d19bc8190bbdc20178239bae1 |
completed | March 28, 2026, 1:32 a.m. |
Created at: March 27, 2026, 2:21 p.m.