Triple
T26708318
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pitohui |
E673338
|
entity |
| Predicate | discoveredAsPoisonousBy |
P167101
|
FINISHED |
| Object | Jack Dumbacher |
—
|
NE NERFINISHED |
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: Jack Dumbacher | Statement: [Pitohui, discoveredAsPoisonousBy, Jack Dumbacher]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: discoveredAsPoisonousBy Context triple: [Pitohui, discoveredAsPoisonousBy, Jack Dumbacher]
-
A.
poisonUsed
Indicates that one entity employed poison as a means to harm, kill, or incapacitate another entity.
-
B.
hasToxicSap
Indicates that an entity produces or contains sap that is harmful or poisonous to other organisms.
-
C.
containsVenomousSpecies
Indicates that the referenced entity includes within it one or more species that are venomous.
-
D.
deadliestFor
Indicates that one entity causes the greatest number of deaths or is most lethal specifically with respect to another entity or group.
-
E.
toxicTo
Indicates that one entity causes harm, poisoning, or adverse effects to another when exposed or applied.
- 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_69eecda3a22881908f3061c760b9d542 |
completed | April 27, 2026, 2:44 a.m. |
| NER | Named-entity recognition | batch_69f6653ccf648190b65fb1141928e47e |
completed | May 2, 2026, 8:57 p.m. |
| PD | Predicate disambiguation | batch_69f6633451948190bcc0410602bb4914 |
completed | May 2, 2026, 8:48 p.m. |
| PDg | Predicate description generation | batch_69f663ff176c8190aaadb475f75daee4 |
completed | May 2, 2026, 8:52 p.m. |
Created at: April 27, 2026, 3:34 a.m.