Triple
T38082970
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Orang Pendek |
E950901
|
entity |
| Predicate | footprintReports |
P189995
|
FINISHED |
| Object | small human-like tracks |
—
|
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: small human-like tracks | Statement: [Orang Pendek, footprintReports, small human-like tracks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: footprintReports Context triple: [Orang Pendek, footprintReports, small human-like tracks]
-
A.
hasPhysicalFootprint
Indicates that one entity occupies or affects a specific physical area or space in the real world.
-
B.
codeFootprint
Indicates the extent or size of code associated with an entity, such as how much code it contains, uses, or impacts.
-
C.
noiseFootprint
Indicates the extent and distribution of noise generated by a source over a surrounding area or environment.
-
D.
hasCarbonFootprintCategory
Indicates that an entity is associated with a specific classification of its carbon footprint level or impact.
-
E.
environmentalReputation
Indicates the perceived quality or standing of an entity’s environmental practices, impact, or responsibility.
- 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_69f76f03a3608190a73fd6df87c792a8 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fc4748843c8190931432653be4890c |
completed | May 7, 2026, 8:03 a.m. |
| PD | Predicate disambiguation | batch_69fc45646ce481908caf292ff9f06e15 |
completed | May 7, 2026, 7:55 a.m. |
| PDg | Predicate description generation | batch_69fc4747b06c8190a3ea5331f02eedad |
completed | May 7, 2026, 8:03 a.m. |
Created at: May 3, 2026, 4:21 p.m.