Triple
T23161159
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Becher's Brook |
E578587
|
entity |
| Predicate | appearsAsFenceNumber |
P151152
|
FINISHED |
| Object | 6th fence on first circuit of Grand National |
—
|
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: 6th fence on first circuit of Grand National | Statement: [Becher's Brook, appearsAsFenceNumber, 6th fence on first circuit of Grand National]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: appearsAsFenceNumber Context triple: [Becher's Brook, appearsAsFenceNumber, 6th fence on first circuit of Grand National]
-
A.
numberOfFences
Indicates the quantity of fences associated with or present around a given entity.
-
B.
hasGateNumber
Indicates that an entity (such as a flight or departure) is associated with a specific gate number.
-
C.
hasAreaNumber
Indicates that an entity is associated with a specific area identified by a numerical code.
-
D.
boroughNumber
Indicates the numerical identifier assigned to a specific borough within a larger administrative or municipal division.
-
E.
ayahNumber
Indicates the specific verse number assigned to an ayah within a surah or text.
- 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_69e245fc75348190a0288401044c8af8 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18f006930819097aafef87405d737 |
completed | April 29, 2026, 4:54 a.m. |
| PD | Predicate disambiguation | batch_69ef89ff76808190808ee4ad9dea776b |
completed | April 27, 2026, 4:08 p.m. |
| PDg | Predicate description generation | batch_69ef9b75e2708190ba48875e36f983bc |
completed | April 27, 2026, 5:23 p.m. |
Created at: April 17, 2026, 4:02 p.m.