Triple
T10166160
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Terumah |
E235208
|
entity |
| Predicate | mentionedInBook |
P5301
|
FINISHED |
| Object | Numbers |
E19391
|
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: Numbers | Statement: [Terumah, mentionedInBook, Numbers]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Numbers Context triple: [Terumah, mentionedInBook, Numbers]
-
A.
Numbers
Numbers is Apple's spreadsheet application for macOS and iOS, used to create, analyze, and visualize data in tables and charts.
-
B.
Numbers
chosen
Numbers is the fourth book of the Hebrew Bible and the Christian Old Testament, recounting the Israelites’ wilderness wanderings and organizing laws and censuses.
-
C.
Digits
"Digits" is a trap-influenced hip-hop single by Young Thug known for its melodic delivery and distinctive production.
-
D.
On Numbers
"On Numbers" is a lost philosophical treatise by the Neopythagorean thinker Numenius of Apamea, likely exploring the metaphysical and symbolic significance of numbers.
-
E.
NUM
NUM is a common abbreviation for the National University of Mongolia, the country’s oldest and largest public university and a leading center of higher education and research in Mongolia.
- 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_69ca84ceafd0819085828600e11bed6b |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdec6cf27481909f38839452ac0e37 |
completed | April 2, 2026, 4:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d300e06bbc8190a51f872eccac8f40 |
completed | April 6, 2026, 12:40 a.m. |
Created at: March 30, 2026, 9:10 p.m.