Triple
T510060
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Granary Burying Ground |
E10586
|
entity |
| Predicate | numberOfMarkers |
P14556
|
FINISHED |
| Object | approximately 2300 |
—
|
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 2300 | Statement: [Granary Burying Ground, numberOfMarkers, approximately 2300]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfMarkers Context triple: [Granary Burying Ground, numberOfMarkers, approximately 2300]
-
A.
numberOfIndicators
Indicates the total count of indicators associated with or relevant to a given entity or context.
-
B.
numberOfTargets
Indicates the quantity of target entities associated with or affected by a given subject or event.
-
C.
hasMarker
Indicates that one entity possesses, is associated with, or is identified by a specific marker.
-
D.
arrowCount
Indicates the number of arrows associated with or involved in a given entity or interaction.
-
E.
numberOfPositions
Indicates the total count of distinct positions or roles associated with a given entity.
- 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_69a2e848adf881908e5e04f7af030093 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f164a9d48190b525a97b5c06ffe2 |
completed | Feb. 28, 2026, 1:45 p.m. |
| PD | Predicate disambiguation | batch_69a2edfe236481909901cc7d4281b33c |
completed | Feb. 28, 2026, 1:30 p.m. |
| PDg | Predicate description generation | batch_69a2eebbd70481908b462296671de67b |
completed | Feb. 28, 2026, 1:33 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.