Triple
T4813958
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nigel |
E107138
|
entity |
| Predicate | historicalForm |
P4181
|
FINISHED |
| Object | Nigellus |
E471593
|
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: Nigellus | Statement: [Nigel, historicalForm, Nigellus]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nigellus Context triple: [Nigel, historicalForm, Nigellus]
-
A.
Nigellus
chosen
Nigellus is a Latinized medieval given name that serves as the root form from which the modern English name Nigel developed.
-
B.
Cauldron Snout
Cauldron Snout is a long, rocky cascade on the River Tees in northern England, known for its dramatic scenery along the Pennine Way.
-
C.
Severus
Severus (Libius Severus) was a Western Roman Emperor who ruled from 461 to 465 AD during the empire’s late, turbulent decline.
-
D.
Holdsclaw
Holdsclaw is the surname of Chamique Holdsclaw, a prominent former American professional basketball player and Women’s Basketball Hall of Famer.
-
E.
Basilisk
The Basilisk is a gigantic, deadly serpent from the Harry Potter series whose gaze can kill and whose venom is among the most lethal magical substances.
- 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_69bd43f779448190b92885cb70abb6c2 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6c80ce048190a7c9f14431d7c62f |
completed | March 20, 2026, 3:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be5ca8c29081909701bfd4ea60586d |
completed | March 21, 2026, 8:54 a.m. |
Created at: March 20, 2026, 1:23 p.m.