Triple
T25471264
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abbas Montis Cassini |
E638309
|
entity |
| Predicate | placeNameInTitle |
P153786
|
FINISHED |
| Object | Mons Cassinus (Monte Cassino) |
—
|
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: Mons Cassinus (Monte Cassino) | Statement: [Abbas Montis Cassini, placeNameInTitle, Mons Cassinus (Monte Cassino)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: placeNameInTitle Context triple: [Abbas Montis Cassini, placeNameInTitle, Mons Cassinus (Monte Cassino)]
-
A.
cityName
Indicates that the associated value is the name of a city.
-
B.
titleForCity
Indicates the official or commonly used title or designation assigned to a particular city.
-
C.
hasPlaceNamesakeIn
Indicates that something is named after a particular place or location.
-
D.
hasNotableToponym
Indicates that an entity is associated with a place name that is particularly notable, distinctive, or significant.
-
E.
alternateNameForPlaceInTitle
chosen
Indicates that a work’s title uses an alternative name or variant designation for a particular place.
- 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_69e75db9b964819096802dcf502e577e |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f6352fdb788190b9bad30243690743 |
completed | May 2, 2026, 5:32 p.m. |
| PD | Predicate disambiguation | batch_69f63182f1408190bddc1214fcbd6145 |
completed | May 2, 2026, 5:16 p.m. |
Created at: April 21, 2026, 2:23 p.m.