Triple
T23884443
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Long Count |
E600289
|
entity |
| Predicate | definesTun |
P153939
|
FINISHED |
| Object | 18 winal (360 days) |
—
|
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: 18 winal (360 days) | Statement: [Long Count, definesTun, 18 winal (360 days)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: definesTun Context triple: [Long Count, definesTun, 18 winal (360 days)]
-
A.
tunnelType
Indicates the specific kind or classification of a tunnel associated with an entity.
-
B.
tunnelName
Indicates the name assigned to a specific tunnel in the relationship or data model.
-
C.
tunnels
Indicates that one entity passes through, under, or within another entity via a tunnel-like passage or structure.
-
D.
tuningType
Indicates the specific method or configuration by which something is adjusted or calibrated to achieve a desired performance or behavior.
-
E.
tunable
Indicates that the associated entity can be adjusted or configured to different settings or values, typically to optimize performance or behavior.
- 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_69e295318e148190b9979d8fc02e168f |
completed | April 17, 2026, 8:16 p.m. |
| NER | Named-entity recognition | batch_69f1ccfbbe4c819093e590709719ab72 |
completed | April 29, 2026, 9:18 a.m. |
| PD | Predicate disambiguation | batch_69f1614e24b48190a1c8fb5b7c75ee0f |
completed | April 29, 2026, 1:39 a.m. |
| PDg | Predicate description generation | batch_69f167dca3608190ace9d2eef56b2af6 |
completed | April 29, 2026, 2:07 a.m. |
Created at: April 17, 2026, 8:24 p.m.