Triple
T4978587
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mending Wall |
E111826
|
entity |
| Predicate | proverbUsed |
P58306
|
FINISHED |
| Object | Good fences make good neighbors. |
—
|
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: Good fences make good neighbors. | Statement: [Mending Wall, proverbUsed, Good fences make good neighbors.]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: proverbUsed Context triple: [Mending Wall, proverbUsed, Good fences make good neighbors.]
-
A.
usedPhrase
Indicates that one entity employed or expressed a particular phrase in speech, writing, or another form of communication.
-
B.
isFamouslyUsedIn
chosen
Indicates that something is widely recognized or well-known for being used in a particular context, work, or situation.
-
C.
usedToExplain
Indicates that one entity serves as an explanation or clarification for another entity.
-
D.
usedWith
Indicates that one entity is typically or appropriately employed together with another entity in a combined or complementary use.
-
E.
quoteProvision
Indicates that one entity supplies or presents a quotation or price estimate to another entity.
- 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_69bd441adc208190b70a033a0741d01e |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd730a7590819088ab8d49c5c88c2f |
completed | March 20, 2026, 4:17 p.m. |
| PD | Predicate disambiguation | batch_69bd7146e6e881908a55ab2756b631f6 |
completed | March 20, 2026, 4:09 p.m. |
Created at: March 20, 2026, 1:33 p.m.