Triple
T31687390
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Barony of England |
E808696
|
entity |
| Predicate | lowestRankOf |
P170703
|
FINISHED |
| Object | English nobility |
—
|
NE NERFINISHED |
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: English nobility | Statement: [Barony of England, lowestRankOf, English nobility]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lowestRankOf Context triple: [Barony of England, lowestRankOf, English nobility]
-
A.
lowestRank
Indicates that the subject has the least or worst rank in an ordered set compared to all other related entities.
-
B.
lowestOrder
chosen
Indicates that one entity has the smallest or minimal order, rank, or priority relative to a set of comparable entities.
-
C.
lowerRank
Indicates that one entity holds an inferior or subordinate rank, status, or position relative to another entity.
-
D.
lowerRankedOrder
Indicates that one entity holds a lower rank or priority in an ordered sequence relative to another entity.
-
E.
lowestScore
Indicates that the associated value is the smallest (minimum) score among a set of scores.
- 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_69f348ddcbc48190950cabcc25ff29b3 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6aaf50be08190a2b62a6d881f8aee |
completed | May 3, 2026, 1:55 a.m. |
| PD | Predicate disambiguation | batch_69f6aa20a1588190a53533fc9764efb2 |
completed | May 3, 2026, 1:51 a.m. |
Created at: April 30, 2026, 11:07 p.m.