Triple
T33839447
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apennine Mountains (Moon) |
E867318
|
entity |
| Predicate | eponymLatinName |
P177766
|
FINISHED |
| Object | Montes Apenninus |
—
|
NE NERFINISHED |
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: Montes Apenninus | Statement: [Apennine Mountains (Moon), eponymLatinName, Montes Apenninus]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: eponymLatinName Context triple: [Apennine Mountains (Moon), eponymLatinName, Montes Apenninus]
-
A.
eponymFor
Indicates that one entity gives its name to another entity, which is then named after it.
-
B.
eponymOriginCountry
Indicates the country from which the person or entity that gave its name (as an eponym) to something originates.
-
C.
eponymKnownFor
Indicates that a person or entity is widely recognized or named as the source or inspiration for something else (such as a concept, place, or object).
-
D.
eponymProfession
Indicates that a person’s profession is the source of an eponym, i.e., a word or name derived from that professional role.
-
E.
eponymBirthName
Indicates that the object is the original birth name of the person or entity after whom something (the eponym) is named.
- F. None of above. chosen
Provenance (4 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_69f34992ad40819087760ed939bd2a7a |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f7009d39508190af7301f824615e88 |
completed | May 3, 2026, 8 a.m. |
| PD | Predicate disambiguation | batch_69f6fc59518081908b0275f47721d561 |
completed | May 3, 2026, 7:42 a.m. |
| PDg | Predicate description generation | batch_69f6ffb7554881908993d6d2ffbcf8f5 |
completed | May 3, 2026, 7:56 a.m. |
Created at: May 1, 2026, 1:47 a.m.