Triple
T9890246
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shenzhou 12 |
E181432
|
entity |
| Predicate | firstForChina |
P59622
|
FINISHED |
| Object | first crewed mission to Tianhe core module |
—
|
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: first crewed mission to Tianhe core module | Statement: [Shenzhou 12, firstForChina, first crewed mission to Tianhe core module]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstForChina Context triple: [Shenzhou 12, firstForChina, first crewed mission to Tianhe core module]
-
A.
firstForCountry
chosen
Indicates that the subject is the first instance or occurrence of its type to happen or exist within the specified country.
-
B.
first
Indicates that one entity precedes all others in an ordered sequence or ranking.
-
C.
firstTitleFor
Indicates that one entity is the earliest or primary title assigned to another entity, typically among multiple possible titles.
-
D.
firstToFeature
Indicates that one entity was the earliest or initial subject to exhibit, include, or present another entity in a given context.
-
E.
firstSeries
Indicates that one entity is the initial or earliest series in an ordered sequence of related series.
- 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_69ca8283a6708190801af7a25a7ebb9f |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cdb47dfa908190884e96e5e5d6f41f |
completed | April 2, 2026, 12:12 a.m. |
| PD | Predicate disambiguation | batch_69cd1d872d50819096b7ab166a8decf1 |
completed | April 1, 2026, 1:28 p.m. |
Created at: March 30, 2026, 8:39 p.m.