Triple
T21413764
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boontling |
E528244
|
entity |
| Predicate | hasNotableWordFormationProcess |
P73391
|
FINISHED |
| Object | eponymy |
—
|
LITERAL 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: eponymy | Statement: [Boontling, hasNotableWordFormationProcess, eponymy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableWordFormationProcess Context triple: [Boontling, hasNotableWordFormationProcess, eponymy]
-
A.
hasPrimaryWordFormationProcess
chosen
Indicates that one entity is the main or predominant word-formation process by which another entity (typically a word or lexical item) is formed.
-
B.
hasWordFormation
Indicates that one linguistic unit is formed or derived from another through a specific word-formation process (such as compounding, derivation, or affixation).
-
C.
typicalWordFormation
Indicates the usual or most common way in which a word is formed from other linguistic elements (such as roots, affixes, or compounds).
-
D.
notableMorphology
Indicates that an entity is characterized by a distinctive or noteworthy physical form, structure, or shape.
-
E.
hasTwoWordForm
Indicates that an entity is represented or expressed using a form consisting of exactly two words.
- F. None of above.
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_69e0c454c248819093425d1099101c09 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e8b201ce1481908392c77e5ca40f5f |
completed | April 22, 2026, 11:33 a.m. |
| PD | Predicate disambiguation | batch_69e61633f8208190a2a849457c4e4198 |
completed | April 20, 2026, 12:04 p.m. |
Created at: April 16, 2026, 5:44 p.m.