Triple
T25879185
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Citeureup |
E651996
|
entity |
| Predicate | hasIndustrialEstates |
P48411
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Citeureup, hasIndustrialEstates, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasIndustrialEstates Context triple: [Citeureup, hasIndustrialEstates, true]
-
A.
hasIndustrialAndOfficeParks
chosen
Indicates that an entity possesses or contains designated areas used for industrial facilities and office complexes.
-
B.
containsIndustrialAreas
Indicates that one entity includes or encompasses industrial areas within its boundaries or scope.
-
C.
hasIndustrialZoneAlong
Indicates that an industrial zone is located adjacent to or extending along the length of a specified linear feature (such as a road, river, or boundary).
-
D.
hasIndustrialPark
Indicates that a location or entity possesses or contains an industrial park within its area or jurisdiction.
-
E.
hasIndustrialAreaType
Indicates that an entity’s industrial area is classified as a specific type or category of industrial zone.
- 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_69e7ab3ad9d88190841ddcb93ab02e96 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f6ffbad8848190867c2988c0ceb84f |
completed | May 3, 2026, 7:56 a.m. |
| PD | Predicate disambiguation | batch_69f6fc53f4f881908dcc698687bbb64d |
completed | May 3, 2026, 7:42 a.m. |
Created at: April 22, 2026, 8:13 a.m.