Triple
T9833885
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | diphtheria antitoxin |
E239052
|
entity |
| Predicate | notEffectiveAgainst |
P84339
|
FINISHED |
| Object | bacterial colonization |
—
|
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: bacterial colonization | Statement: [diphtheria antitoxin, notEffectiveAgainst, bacterial colonization]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notEffectiveAgainst Context triple: [diphtheria antitoxin, notEffectiveAgainst, bacterial colonization]
-
A.
effectivenessAgainst
chosen
Indicates how well one entity performs in countering, influencing, or mitigating the impact of another entity.
-
B.
hasWeakness
Indicates that one entity is vulnerable to, or can be adversely affected or defeated by, another entity.
-
C.
usedAgainst
Indicates that one entity is employed, applied, or deployed in opposition to, or for the purpose of affecting, another entity.
-
D.
methodOfDefeat
Indicates the specific way or technique by which one entity defeats or overcomes another.
-
E.
isResistant
Indicates that an entity can withstand, oppose, or is not significantly affected by a specified force, influence, or agent.
- 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_69ca84e314108190978324a4bdb959f8 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb3385054819094145c96204e3f0d |
completed | April 2, 2026, 12:07 a.m. |
| PD | Predicate disambiguation | batch_69cd03e30bc08190816c0a6d29c21b0f |
completed | April 1, 2026, 11:39 a.m. |
Created at: March 30, 2026, 8:32 p.m.