Triple
T36023974
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | municipality of Teylingen |
E1042069
|
entity |
| Predicate | hasCharacteristicIndustry |
P198113
|
FINISHED |
| Object | flower bulb cultivation |
—
|
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: flower bulb cultivation | Statement: [municipality of Teylingen, hasCharacteristicIndustry, flower bulb cultivation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCharacteristicIndustry Context triple: [municipality of Teylingen, hasCharacteristicIndustry, flower bulb cultivation]
-
A.
hasPrincipalIndustry
Indicates that an entity’s main or primary industry of operation is the specified industry.
-
B.
containsIndustry
Indicates that one entity includes or encompasses a particular industry within its scope, structure, or operations.
-
C.
hasTypicalIndustry
chosen
Indicates that an entity is commonly or characteristically associated with a particular industry or sector.
-
D.
hasIndustrySection
Indicates that an entity belongs to, is categorized under, or is associated with a particular industry section.
-
E.
hasBusinessModelCharacteristic
Indicates that a business model possesses or exhibits a specific feature, quality, or attribute.
- 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_69f76e2c568881909e1e21f85252b0f0 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fef2db323c8190821bda53f22a42be |
completed | May 9, 2026, 8:39 a.m. |
| PD | Predicate disambiguation | batch_69fef21d63c88190abf6a99b59b3c655 |
completed | May 9, 2026, 8:36 a.m. |
Created at: May 3, 2026, 4:07 p.m.