Triple
T25913567
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Port Authority Building, Antwerp |
E652963
|
entity |
| Predicate | numberOfEmployeesHosted |
P192548
|
FINISHED |
| Object | approximately 500 |
—
|
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: approximately 500 | Statement: [Port Authority Building, Antwerp, numberOfEmployeesHosted, approximately 500]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfEmployeesHosted Context triple: [Port Authority Building, Antwerp, numberOfEmployeesHosted, approximately 500]
-
A.
guestCountApproximate
Indicates that the number of guests involved is represented as an estimated or approximate count rather than an exact figure.
-
B.
numberOfHosts
Indicates the total count of distinct hosts associated with or involved in a given entity or event.
-
C.
numberOfParticipants
Indicates the total count of entities involved in a particular event, activity, or relationship.
-
D.
numberOfPersons
Indicates the total count of individual persons associated with or involved in a given entity, event, or context.
-
E.
numberOfMembersSeated
Indicates the count of members who are currently seated in a given context or setting.
- 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_69e7ab3e025c819086771607157f0015 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69fd19f791f48190bbb6f6047f9ddc59 |
completed | May 7, 2026, 11:02 p.m. |
| PD | Predicate disambiguation | batch_69fd0df365948190bc9bfc7ffd46acd8 |
completed | May 7, 2026, 10:10 p.m. |
| PDg | Predicate description generation | batch_69fd19f6a7888190aa7eee2b87687c53 |
completed | May 7, 2026, 11:02 p.m. |
Created at: April 22, 2026, 8:30 a.m.