Triple
T1898094
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shanghai Railway Station |
E37626
|
entity |
| Predicate | hasSecurityCheck |
P22957
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Shanghai Railway Station, hasSecurityCheck, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSecurityCheck Context triple: [Shanghai Railway Station, hasSecurityCheck, yes]
-
A.
hasSecurityPresence
chosen
Indicates that some form of security personnel, system, or measures are present at or associated with an entity or location.
-
B.
hasSecuritySupport
Indicates that one entity provides security-related assistance, maintenance, or protection services for another entity.
-
C.
hasSecurityArea
Indicates that an entity is associated with, assigned to, or falls within a defined security-controlled area or zone.
-
D.
hasSecurityNotion
Indicates that one entity possesses, defines, or is associated with a particular concept or notion of security in relation to another entity or context.
-
E.
hasSecurityConsideration
Indicates that there is a relevant security-related issue, risk, or precaution associated with the referenced entity.
- 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_69a8861be7148190a680937ec451a304 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb170657481908662089511a8f301 |
completed | March 7, 2026, 5:02 a.m. |
| PD | Predicate disambiguation | batch_69abafe7e7e88190b58c0df59187c0c2 |
completed | March 7, 2026, 4:56 a.m. |
Created at: March 4, 2026, 7:35 p.m.