Triple
T34281582
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Port of Catoosa |
E879602
|
entity |
| Predicate | hasNumberOfTenants |
P74743
|
FINISHED |
| Object | dozens of industrial and manufacturing companies |
—
|
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: dozens of industrial and manufacturing companies | Statement: [Port of Catoosa, hasNumberOfTenants, dozens of industrial and manufacturing companies]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfTenants Context triple: [Port of Catoosa, hasNumberOfTenants, dozens of industrial and manufacturing companies]
-
A.
hasTenants
Indicates that an entity occupies or rents space from another entity as its tenant.
-
B.
supportsMultiTenancy
Indicates that the subject system or component is capable of serving and isolating multiple distinct tenants or customer environments within a single deployment.
-
C.
numberOfTenants
chosen
Indicates the quantity of tenants associated with a given entity or property.
-
D.
numberOfAnchorTenants
Indicates the count of primary or major tenants associated with a given property or location.
-
E.
hasTenantsOverTime
Indicates that an entity has one or more tenants associated with it during specific periods over time.
- 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_69f349b5f6648190b9420d94a4cd16e0 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69fe991bca608190b524e419642f4243 |
completed | May 9, 2026, 2:16 a.m. |
| PD | Predicate disambiguation | batch_69fe979fc1c4819091fc48d63ea12063 |
completed | May 9, 2026, 2:10 a.m. |
Created at: May 1, 2026, 1:57 a.m.