Triple
T7777330
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | N1 Manpower, Personnel, Training and Education |
E221425
|
entity |
| Predicate | usesStaffCode |
P78937
|
FINISHED |
| Object | N1 |
—
|
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: N1 | Statement: [N1 Manpower, Personnel, Training and Education, usesStaffCode, N1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesStaffCode Context triple: [N1 Manpower, Personnel, Training and Education, usesStaffCode, N1]
-
A.
usesBookingCode
Indicates that one entity makes use of a specific booking code associated with another entity or transaction.
-
B.
usesNPCCode
Indicates that one entity employs or relies on the NPC code associated with another entity for its operation or identification.
-
C.
usesLineCode
Indicates that one entity employs or references a specific line code as part of its operation, identification, or communication.
-
D.
usesCodeName
Indicates that one entity refers to another entity by a code name or alias instead of its real or full designation.
-
E.
hasStationCode
Indicates that an entity is associated with a specific station identification code.
- 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_69ca83ebbef881909ac47f789145fef7 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cae7e779ec8190b77296d9c2ac3210 |
completed | March 30, 2026, 9:15 p.m. |
| PD | Predicate disambiguation | batch_69caa488532c819093ac40bba0b3c7ef |
completed | March 30, 2026, 4:27 p.m. |
| PDg | Predicate description generation | batch_69cae7e47c5c8190bca90d45b3cdc25e |
completed | March 30, 2026, 9:15 p.m. |
Created at: March 30, 2026, 4:14 p.m.