Triple
T270420
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ARP |
E5620
|
entity |
| Predicate | mapsTo |
P9923
|
FINISHED |
| Object | hardware address |
—
|
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: hardware address | Statement: [ARP, mapsTo, hardware address]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mapsTo Context triple: [ARP, mapsTo, hardware address]
-
A.
mapProjection
Indicates the specific method or transformation used to represent locations from the curved surface of the Earth onto a flat map.
-
B.
mapCoverage
Indicates the extent or area that is represented, covered, or included by a particular map.
-
C.
connectsTo
Indicates a relationship where one entity is linked or joined to another, allowing interaction, communication, or transfer between them.
-
D.
couplesTo
Indicates that one entity is joined or linked to another in a way that allows interaction, transfer, or mutual influence between them.
-
E.
cityPanorama
Indicates a wide, comprehensive visual view or representation of a cityscape, typically encompassing many of its features in a single scene.
- 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_69a25853594c8190b05ec3a586ec88bf |
completed | Feb. 28, 2026, 2:52 a.m. |
| NER | Named-entity recognition | batch_69a25e69a9248190b9e7959b43223baa |
completed | Feb. 28, 2026, 3:18 a.m. |
| PD | Predicate disambiguation | batch_69a25b721180819080d43c43fcbccf87 |
completed | Feb. 28, 2026, 3:05 a.m. |
| PDg | Predicate description generation | batch_69a25e68f0408190bfc851c32d6eebf3 |
completed | Feb. 28, 2026, 3:18 a.m. |
Created at: Feb. 28, 2026, 2:57 a.m.