Triple
T25384986
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bloodpack enforcer |
E631496
|
entity |
| Predicate | rangeSpecialization |
P159435
|
FINISHED |
| Object | short range |
—
|
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: short range | Statement: [Bloodpack enforcer, rangeSpecialization, short range]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rangeSpecialization Context triple: [Bloodpack enforcer, rangeSpecialization, short range]
-
A.
regionSpecialization
Indicates that a region is designated or recognized as being particularly focused on, adapted to, or specialized in a specific function, activity, or domain.
-
B.
specializationRegion
Indicates that something is specialized, adapted, or specifically applicable to a particular geographic or spatial region.
-
C.
exportSpecialization
Indicates a relationship where one entity specializes in exporting particular goods, services, or resources to another entity or market.
-
D.
branchSpecialization
Indicates that one branch or subdivision is specialized or focused in a particular area, function, or domain relative to others.
-
E.
positionSpecialization
Indicates that one position is a more specialized or focused variant of another, broader position.
- 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_69e75a8c50788190aabaa9f96710fc43 |
completed | April 21, 2026, 11:07 a.m. |
| NER | Named-entity recognition | batch_69f584f07b648190aee894c1d5320bc3 |
completed | May 2, 2026, 5 a.m. |
| PD | Predicate disambiguation | batch_69f4a0f7c6008190ae8cee3e71e19b94 |
completed | May 1, 2026, 12:47 p.m. |
| PDg | Predicate description generation | batch_69f55e497fa081909bc59a7b92c5df59 |
completed | May 2, 2026, 2:15 a.m. |
Created at: April 21, 2026, 1:46 p.m.