Triple
T11466612
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ziehl–Neelsen stain |
E271795
|
entity |
| Predicate | sensitivityComparedToCulture |
P99731
|
FINISHED |
| Object | lower |
—
|
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: lower | Statement: [Ziehl–Neelsen stain, sensitivityComparedToCulture, lower]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sensitivityComparedToCulture Context triple: [Ziehl–Neelsen stain, sensitivityComparedToCulture, lower]
-
A.
basedInCulture
Indicates that an entity is situated within, originates from, or is fundamentally associated with a particular culture.
-
B.
locale
Indicates that one entity is the place, setting, or geographic area in which another entity exists, occurs, or is situated.
-
C.
caseSensitivityVariant
Indicates that one string or textual form is a variant of another that differs only in letter casing (e.g., uppercase vs lowercase).
-
D.
culturallySimilarTo
Indicates that two entities share comparable cultural characteristics, practices, or values.
-
E.
localeType
Indicates the classification or category of a locale (such as region, city, or venue type) that characterizes the kind of place involved in the relationship.
- 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_69d6aae0c8d881908a5a360c0be3242e |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d822f5eb988190b309b8e309f6d1a5 |
completed | April 9, 2026, 10:06 p.m. |
| PD | Predicate disambiguation | batch_69d80867ff248190bb157fa9e355353b |
completed | April 9, 2026, 8:13 p.m. |
| PDg | Predicate description generation | batch_69d822ef46988190a1c360da4ee14fef |
completed | April 9, 2026, 10:06 p.m. |
Created at: April 8, 2026, 9:35 p.m.