Triple
T7910814
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tacony |
E183692
|
entity |
| Predicate | alsoContains |
P79729
|
FINISHED |
| Object | light industrial areas |
—
|
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: light industrial areas | Statement: [Tacony, alsoContains, light industrial areas]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: alsoContains Context triple: [Tacony, alsoContains, light industrial areas]
-
A.
containedWith
Indicates that one entity is located or kept inside the bounds or interior space of another entity.
-
B.
containsMostOf
Indicates that one entity includes the majority (but not necessarily all) of the substance, elements, or components of another entity.
-
C.
alsoHolds
Indicates that a condition, property, or relation that applies in one context or case simultaneously applies in another context or case.
-
D.
areSupersetOf
Indicates that one set contains all elements of another set, possibly along with additional elements.
-
E.
includedWith
Indicates that one entity is provided or packaged together as part of another entity.
- 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_69ca828dec0c81908b8f55a4dbbb53ff |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3a725b8c8190a530adb3107a95dd |
completed | March 31, 2026, 3:07 a.m. |
| PD | Predicate disambiguation | batch_69cae92f9498819085277879e59aa072 |
completed | March 30, 2026, 9:20 p.m. |
| PDg | Predicate description generation | batch_69caf7882b048190baa333af9f698590 |
completed | March 30, 2026, 10:22 p.m. |
Created at: March 30, 2026, 5:04 p.m.