Triple
T9325873
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GS1 |
E224383
|
entity |
| Predicate | primaryAreaOfWork |
P3
|
FINISHED |
| Object | automatic identification and data capture |
—
|
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: automatic identification and data capture | Statement: [GS1, primaryAreaOfWork, automatic identification and data capture]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryAreaOfWork Context triple: [GS1, primaryAreaOfWork, automatic identification and data capture]
-
A.
primaryWork
Indicates that one work is the main or most significant work associated with a given entity, as opposed to other secondary or related works.
-
B.
primaryArea
Indicates that one entity is the main or most important area, domain, or field associated with another entity.
-
C.
primaryBusinessArea
Indicates the main field, sector, or domain in which an entity primarily conducts its business activities.
-
D.
fieldOfWork
chosen
Indicates the professional or academic domain in which an entity is primarily engaged or specializes.
-
E.
primaryInterest
Indicates that one entity is the main or most significant focus of attention, concern, or engagement for another entity.
- F. None of above.
Provenance (3 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_69ca8427a0c08190b749831d5ea98f02 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd36f88e988190bb896a3d7c3c723c |
completed | April 1, 2026, 3:17 p.m. |
| PD | Predicate disambiguation | batch_69cc7a643924819097f01144734901cf |
completed | April 1, 2026, 1:52 a.m. |
Created at: March 30, 2026, 7:39 p.m.