Triple
T30164887
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Puzrish-Dagan |
E766767
|
entity |
| Predicate | tabletArchiveType |
P168511
|
FINISHED |
| Object | economic texts |
—
|
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: economic texts | Statement: [Puzrish-Dagan, tabletArchiveType, economic texts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tabletArchiveType Context triple: [Puzrish-Dagan, tabletArchiveType, economic texts]
-
A.
targetFormFactor
Indicates the specific physical configuration or design format that something is intended to be used with or fit into.
-
B.
tabletCount
Indicates the number of tablets associated with or allocated to a given entity or context.
-
C.
mobilePackageType
Indicates the specific type or category of a mobile service package associated with an entity.
-
D.
testedDeviceType
Indicates that one entity is the type or category of device on which another entity has been tested.
-
E.
touchscreenType
Indicates the specific kind or technology of touchscreen associated with an entity.
- 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_69f2247a968881909d79c18f2bfcb275 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f67f06825c819088cfc20337be8319 |
completed | May 2, 2026, 10:47 p.m. |
| PD | Predicate disambiguation | batch_69f673c7a4588190837854f3ef61e6bf |
completed | May 2, 2026, 9:59 p.m. |
| PDg | Predicate description generation | batch_69f6749f205c81909d1aacf462912eee |
completed | May 2, 2026, 10:03 p.m. |
Created at: April 29, 2026, 7:22 p.m.