Triple
T11325857
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Philip III, Count of Nassau-Weilburg |
E268214
|
entity |
| Predicate | hasOrdinal |
P4901
|
FINISHED |
| Object | III |
—
|
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: III | Statement: [Philip III, Count of Nassau-Weilburg, hasOrdinal, III]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOrdinal Context triple: [Philip III, Count of Nassau-Weilburg, hasOrdinal, III]
-
A.
hasOrdinalSeries
Indicates that one entity belongs to, or is positioned within, an ordered sequence or series relative to other entities.
-
B.
usedOrdinal
Indicates that one entity is used as an ordinal indicator or position marker relative to another entity.
-
C.
hasNotableBearerOrdinalNumber
Indicates that an entity is associated with a notable bearer identified by a specific ordinal position (e.g., first, second, third) among others with the same name or title.
-
D.
ordinalNumber
chosen
Indicates the position or rank of an entity within an ordered sequence (e.g., first, second, third).
-
E.
hasRankOrder
Indicates that one entity is ordered or positioned relative to others according to a specific ranking or sequence.
- 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_69d6aacb1f0881908c84a349fd1be047 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e9e2253881909518cad0f12ef612 |
completed | April 9, 2026, 6:03 p.m. |
| PD | Predicate disambiguation | batch_69d787ad575081908274280bf75d95fd |
completed | April 9, 2026, 11:04 a.m. |
Created at: April 8, 2026, 9:32 p.m.