Triple
T29800060
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lucia di Lammermoor |
E756666
|
entity |
| Predicate | firstActCount |
P81018
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [Lucia di Lammermoor, firstActCount, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstActCount Context triple: [Lucia di Lammermoor, firstActCount, 1]
-
A.
secondActCount
Indicates the number of times an entity performs or participates in a second action or event within a defined sequence or context.
-
B.
numberOfActs
Indicates the total count of discrete acts or actions associated with a given entity or event.
-
C.
firstMovement
Indicates that an entity represents the initial or earliest movement, action, or motion in a sequence of movements.
-
D.
firstAppearanceAct
chosen
Indicates the act in which an entity makes its first appearance within a work or performance.
-
E.
firstOrdinary
Indicates that the subject is the first entity to hold or occupy an ordinary (non-special, standard) position, role, or status in a given sequence or context.
- 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_69f22454583081908927516cb9938d1d |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_6a013a251b4081908b43d85dd95586c5 |
completed | May 11, 2026, 2:08 a.m. |
| PD | Predicate disambiguation | batch_6a0137e4ea988190812173a5ff044098 |
completed | May 11, 2026, 1:59 a.m. |
Created at: April 29, 2026, 5:17 p.m.