Triple
T20090193
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oxyrhynchus Papyri |
E496246
|
entity |
| Predicate | estimatedItemsCount |
P120121
|
FINISHED |
| Object | tens of thousands of fragments |
—
|
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: tens of thousands of fragments | Statement: [Oxyrhynchus Papyri, estimatedItemsCount, tens of thousands of fragments]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: estimatedItemsCount Context triple: [Oxyrhynchus Papyri, estimatedItemsCount, tens of thousands of fragments]
-
A.
estimatedNumberOfItems
chosen
Indicates the approximate count or quantity of items associated with the subject, typically when the exact number is unknown or uncertain.
-
B.
estimatedNumberOfRemains
Indicates the approximate count of human or other remains associated with an entity, based on estimation rather than an exact measurement.
-
C.
basketCount
Indicates the number of baskets associated with a given entity or context.
-
D.
sectionCountApproximate
Indicates that the number of sections associated with an entity is known only approximately rather than as an exact count.
-
E.
collectionSize
Indicates the total number of items contained within a specified collection.
- 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_69da626eee3881909f3454986d4a6511 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6655e1ef0819095f40a1ce2eb17b7 |
completed | April 20, 2026, 5:41 p.m. |
| PD | Predicate disambiguation | batch_69e54cf369b88190931532420517dac7 |
completed | April 19, 2026, 9:45 p.m. |
Created at: April 11, 2026, 11:20 p.m.