Triple
T4891224
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Philippa of Lancaster |
E109565
|
entity |
| Predicate | monument |
P33901
|
FINISHED |
| Object | tomb at Batalha Monastery |
—
|
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: tomb at Batalha Monastery | Statement: [Philippa of Lancaster, monument, tomb at Batalha Monastery]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: monument Context triple: [Philippa of Lancaster, monument, tomb at Batalha Monastery]
-
A.
monumentType
Indicates the specific kind or category of monument that an entity is classified as.
-
B.
builtMonument
Indicates that one entity constructed or created a monument in honor of, or related to, another entity.
-
C.
significantMonument
Indicates that something is a monument of notable historical, cultural, or symbolic importance.
-
D.
monumentSubject
chosen
Indicates that the subject serves as the monument or commemorative structure associated with another entity.
-
E.
monumentGroup
Indicates that one monument belongs to, or is categorized within, a specific group or collection of monuments.
- 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_69bd440f71348190b99938e59fb7f9a1 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6ffabccc81909115ece1b04e2061 |
completed | March 20, 2026, 4:04 p.m. |
| PD | Predicate disambiguation | batch_69bd6c2e7b5c8190b8bf9d616dfa24f0 |
completed | March 20, 2026, 3:47 p.m. |
Created at: March 20, 2026, 1:28 p.m.