Triple
T36223677
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Altuglas |
E1047923
|
entity |
| Predicate | hasApplicationSector |
P116570
|
FINISHED |
| Object | architecture |
—
|
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: architecture | Statement: [Altuglas, hasApplicationSector, architecture]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApplicationSector Context triple: [Altuglas, hasApplicationSector, architecture]
-
A.
hasMarketSector
chosen
Indicates that an entity operates within, is associated with, or belongs to a particular market sector or industry segment.
-
B.
hasOccupationSector
Indicates that an entity’s occupation belongs to or is categorized within a particular economic or professional sector.
-
C.
isSectorSpecific
Indicates that something is tailored or restricted to a particular industry or sector rather than being generally applicable.
-
D.
hasSectoralPriority
Indicates that something is designated as having priority or special importance within a particular sector or industry.
-
E.
hasIndustrialSector
Indicates that an entity is associated with, operates in, or belongs to a particular industrial sector or branch of economic activity.
- 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_69f76e42c878819095c8d19c0267fb87 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fe1fd637c08190aa95cd2478c278cb |
completed | May 8, 2026, 5:39 p.m. |
| PD | Predicate disambiguation | batch_69fe19344bb481909b5e2144155e4add |
completed | May 8, 2026, 5:11 p.m. |
Created at: May 3, 2026, 4:09 p.m.