Triple
T29859119
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AMRs |
E758265
|
entity |
| Predicate | advantageOverAGVs |
P168691
|
FINISHED |
| Object | do not require fixed tracks or markers |
—
|
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: do not require fixed tracks or markers | Statement: [AMRs, advantageOverAGVs, do not require fixed tracks or markers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: advantageOverAGVs Context triple: [AMRs, advantageOverAGVs, do not require fixed tracks or markers]
-
A.
advantageOverApps
Indicates that one entity possesses a benefit or superiority when compared to applications (apps).
-
B.
isFullyRobotic
Indicates that the entity operates entirely through robotic mechanisms without human biological components or manual control.
-
C.
advantageOverPhysical
Indicates that something possesses a benefit or superiority when compared to a physical or tangible counterpart.
-
D.
advantageOverDeterministicMethods
Indicates that one method or approach provides a benefit or superior performance compared to deterministic methods.
-
E.
efficiencyComparedToManual
Indicates how the efficiency of a process or system compares to performing the same task manually.
- 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_69f2245b4dec8190b85f664d918a00a5 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f67683fde88190bf2f338ec18dcaca |
completed | May 2, 2026, 10:11 p.m. |
| PD | Predicate disambiguation | batch_69f673c4abec8190bc2379e66f4af0a9 |
completed | May 2, 2026, 9:59 p.m. |
| PDg | Predicate description generation | batch_69f674df80b08190adb7f7531083bbb1 |
completed | May 2, 2026, 10:04 p.m. |
Created at: April 29, 2026, 5:48 p.m.