Triple
T327855
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Palais des Nations |
E6558
|
entity |
| Predicate | numberOfOffices |
P11464
|
FINISHED |
| Object | over 2800 |
—
|
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: over 2800 | Statement: [Palais des Nations, numberOfOffices, over 2800]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfOffices Context triple: [Palais des Nations, numberOfOffices, over 2800]
-
A.
numberOfCountryOffices
Indicates the total count of offices or branches that an organization maintains across different countries.
-
B.
establishedOffice
Indicates that an entity created or set up an official office or place of operation.
-
C.
hasOfficeType
Indicates that an entity’s office is classified as a specific type or category of office.
-
D.
hasOffice
Indicates that an entity possesses or maintains an office at a particular location or within a specific organization.
-
E.
numberOfCourts
Indicates the quantity of courts associated with or present at a given entity or location.
- 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_69a2e7933d6c8190bb2592ad13286ef2 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2ea98fa2c8190a5b44f4a26543a17 |
completed | Feb. 28, 2026, 1:16 p.m. |
| PD | Predicate disambiguation | batch_69a2e94aab1c8190b8654708c87eeb91 |
completed | Feb. 28, 2026, 1:10 p.m. |
| PDg | Predicate description generation | batch_69a2ea09a5e881908b313cb37409a4f9 |
completed | Feb. 28, 2026, 1:13 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.