Triple
T4361804
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Philhellenes |
E98676
|
entity |
| Predicate | hasNotableMember |
P304
|
FINISHED |
| Object | Webster |
E194770
|
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: Webster | Statement: [Philhellenes, hasNotableMember, Webster]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Webster Context triple: [Philhellenes, hasNotableMember, Webster]
-
A.
Webster
chosen
Webster is a common English surname most famously associated with American lexicographer Noah Webster, whose name is linked to influential early American dictionaries.
-
B.
Merriam
Merriam is a surname most notably associated with American zoologist and ethnographer Clinton Hart Merriam.
-
C.
An American Dictionary of the English Language
An American Dictionary of the English Language is Noah Webster’s landmark 1828 dictionary that helped standardize American English spelling and usage.
-
D.
Collins
Collins is a common English-language surname borne by numerous notable individuals across fields such as politics, sports, literature, and entertainment.
-
E.
Collins
Collins is a major aerospace and defense technology company known for providing advanced avionics and aircraft systems to commercial and military customers worldwide.
- 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_69b3454c772081908e20173e379e8ebe |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b351e47d388190b31500189577cd75 |
completed | March 12, 2026, 11:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5dbc357988190a2982a86847e2c42 |
completed | March 14, 2026, 10:05 p.m. |
Created at: March 12, 2026, 11:16 p.m.