Triple
T34744649
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Percopsiformes |
E1001600
|
entity |
| Predicate | eyeAdaptationInSomeMembers |
P81228
|
FINISHED |
| Object | reduced or absent eyes |
—
|
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: reduced or absent eyes | Statement: [Percopsiformes, eyeAdaptationInSomeMembers, reduced or absent eyes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: eyeAdaptationInSomeMembers Context triple: [Percopsiformes, eyeAdaptationInSomeMembers, reduced or absent eyes]
-
A.
hasSensoryAdaptation
chosen
Indicates that an entity possesses a specialized sensory modification or adjustment that enhances its ability to detect, process, or respond to environmental stimuli.
-
B.
eyeFeatureObserved
Indicates that a specific characteristic or condition of the eye has been detected or recorded through observation.
-
C.
hasHumanAdaptation
Indicates that something has been modified, designed, or adjusted specifically to suit human use, abilities, or needs.
-
D.
eyeCharacteristic
Indicates a relationship where an entity possesses a specific attribute, feature, or quality of its eyes.
-
E.
eyeCount
Indicates the number of eyes an entity has.
- 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_69f76db0367081909b57c50a7fb03025 |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69f77ffa6b68819090257fed3802c239 |
completed | May 3, 2026, 5:03 p.m. |
| PD | Predicate disambiguation | batch_69f7795978c481909e152cd1bd02dd07 |
completed | May 3, 2026, 4:35 p.m. |
Created at: May 3, 2026, 3:59 p.m.