Triple
T1788149
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kültepe |
E39434
|
entity |
| Predicate | tabletCount |
P32396
|
FINISHED |
| Object | over 20,000 cuneiform tablets |
—
|
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: over 20,000 cuneiform tablets | Statement: [Kültepe, tabletCount, over 20,000 cuneiform tablets]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tabletCount Context triple: [Kültepe, tabletCount, over 20,000 cuneiform tablets]
-
A.
benchCount
Indicates the number of benches associated with a given entity or location.
-
B.
hasNumberOfScreens
Indicates the quantity of screens associated with or contained in a given entity.
-
C.
supportsDeviceCount
Indicates the number of devices that a system, service, or component is capable of supporting concurrently.
-
D.
deviceIndicates
Indicates that a device provides a signal, status, or output that conveys information about a condition, event, or state.
-
E.
hardwareUsedBy
Indicates that a piece of hardware is utilized or operated by a particular entity (such as a person, system, or organization).
- 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_69a88631854081909723959921e45c2b |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab75457e54819096b8c6ae8c65550c |
completed | March 7, 2026, 12:45 a.m. |
| PD | Predicate disambiguation | batch_69aa61d165688190924962a98e07ff69 |
completed | March 6, 2026, 5:10 a.m. |
| PDg | Predicate description generation | batch_69ab75444d28819091c393e62fc97f82 |
completed | March 7, 2026, 12:45 a.m. |
Created at: March 4, 2026, 7:32 p.m.