Triple
T32385179
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ban Liang |
E827527
|
entity |
| Predicate | associatedWithReformBy |
P75371
|
FINISHED |
| Object | Qin Shi Huang |
E181418
|
NE 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: Qin Shi Huang | Statement: [Ban Liang, associatedWithReformBy, Qin Shi Huang]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithReformBy Context triple: [Ban Liang, associatedWithReformBy, Qin Shi Huang]
-
A.
associatedReform
Indicates a relationship where one entity is linked to, connected with, or involved in a particular reform or set of reforms.
-
B.
associatedReforms
Indicates a relationship where certain reforms are linked or connected to a given entity, such as a policy, event, or individual.
-
C.
associatedWithReformMovement
Indicates that an entity is connected or linked in some way to a reform movement, such as by participation, support, influence, or affiliation.
-
D.
associatedWithLegalReforms
Indicates a relationship where an entity is connected to, involved in, or influenced by specific legal reforms or changes in law.
-
E.
reformsBy
chosen
Indicates that one entity initiates, implements, or is responsible for changes or improvements (reforms) affecting another entity.
- F. None of above.
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_69f349184e7481909c6c54428cb9cf12 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a037c894b488190bcbec2eccaff4a01 |
completed | May 12, 2026, 7:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a34c65c56f0819083545dbf55f31f19 |
completed | June 19, 2026, 4:32 a.m. |
| PD | Predicate disambiguation | batch_6a0379edf2d88190b492fca86ed23cac |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 12:51 a.m.