Triple
T32368455
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tianyou |
E827062
|
entity |
| Predicate | hasEraYearCount |
P173981
|
FINISHED |
| Object | Tianyou 1 to Tianyou 4 |
—
|
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: Tianyou 1 to Tianyou 4 | Statement: [Tianyou, hasEraYearCount, Tianyou 1 to Tianyou 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEraYearCount Context triple: [Tianyou, hasEraYearCount, Tianyou 1 to Tianyou 4]
-
A.
hasEraReset
Indicates that a new era or time period has been initiated, resetting the previous temporal sequence or counting.
-
B.
hasPrimaryEra
Indicates that an entity is chiefly associated with or belongs to a particular historical or temporal era.
-
C.
representsEra
Indicates that one entity designates the historical era, period, or age to which another entity belongs or is associated.
-
D.
regnalYearCount
Indicates the number of years a ruler has reigned, typically counting from the official start of their rule.
-
E.
subjectHasEra
Indicates that a subject is associated with, defined by, or belongs to a particular historical or temporal era.
- 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_69f349166d548190887b412fe908e2f4 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6be9f57048190819d133d4caba58e |
completed | May 3, 2026, 3:18 a.m. |
| PD | Predicate disambiguation | batch_69f6ba6eb32c8190bf405b2011fa48f7 |
completed | May 3, 2026, 3:01 a.m. |
| PDg | Predicate description generation | batch_69f6bb344bb48190a8089f29c0063ded |
completed | May 3, 2026, 3:04 a.m. |
Created at: May 1, 2026, 12:50 a.m.