Triple
T16983263
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zhu Houcong |
E411996
|
entity |
| Predicate | knownForTreatmentOfOfficials |
P125476
|
FINISHED |
| Object | harsh punishments |
—
|
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: harsh punishments | Statement: [Zhu Houcong, knownForTreatmentOfOfficials, harsh punishments]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: knownForTreatmentOfOfficials Context triple: [Zhu Houcong, knownForTreatmentOfOfficials, harsh punishments]
-
A.
knownForTreatmentOf
Indicates that an entity is recognized or notable for providing treatment or medical care for a particular condition, disease, or type of patient.
-
B.
treatmentOf
Indicates a relationship where one entity administers, provides, or is responsible for a therapeutic intervention directed toward another entity (typically a patient or condition).
-
C.
officersKnownAs
Indicates that certain officers are referred to or recognized by a particular name or designation.
-
D.
knownForIssuerTherapies
Indicates that an issuer is recognized or notable for providing or developing specific therapies.
-
E.
associatedWithGovernmentOfficial
Indicates a relationship in which an entity has a connection, involvement, or affiliation with a government official.
- 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_69d886ca8f348190812768ea8d5055ce |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d188ede48190baead48aac84c78d |
completed | April 18, 2026, 6:46 p.m. |
| PD | Predicate disambiguation | batch_69e35d4dff4881909b384e30f2d36bff |
completed | April 18, 2026, 10:30 a.m. |
| PDg | Predicate description generation | batch_69e3753f93c88190808fec5692f66699 |
completed | April 18, 2026, 12:12 p.m. |
Created at: April 10, 2026, 5:32 a.m.