Triple
T19256085
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Geryon |
E481519
|
entity |
| Predicate | hasBodyPartCount |
P135072
|
FINISHED |
| Object | three heads (in some traditions) |
—
|
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: three heads (in some traditions) | Statement: [Geryon, hasBodyPartCount, three heads (in some traditions)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBodyPartCount Context triple: [Geryon, hasBodyPartCount, three heads (in some traditions)]
-
A.
hasLimbs
Indicates that an entity possesses one or more limbs as physical appendages.
-
B.
hasBodyRegion
Indicates that an entity possesses, includes, or is associated with a specific anatomical or bodily region.
-
C.
hasLimb
Indicates that an entity possesses a specific limb as part of its body.
-
D.
bodyNumber
Indicates the numerical position or identifier assigned to a body within a sequence or collection of bodies.
-
E.
hasBodyComposition
Indicates a relationship where an entity possesses or is characterized by a particular makeup or proportion of physical components (such as tissues, substances, or materials) in its body.
- 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_69d8e8cd9d1081908a181d02b88b59b8 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5fb3572c08190a55f1c71cdb18b42 |
completed | April 20, 2026, 10:08 a.m. |
| PD | Predicate disambiguation | batch_69e4dd002d00819088b625056edfb74e |
completed | April 19, 2026, 1:47 p.m. |
| PDg | Predicate description generation | batch_69e4ddcf50108190a09d0f1291c17374 |
completed | April 19, 2026, 1:51 p.m. |
Created at: April 10, 2026, 1:28 p.m.