Triple
T9424955
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lovesexy |
E227242
|
entity |
| Predicate | hasContinuousSequence |
P88809
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Lovesexy, hasContinuousSequence, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasContinuousSequence Context triple: [Lovesexy, hasContinuousSequence, true]
-
A.
hasTypicalSequence
Indicates that there is a usual or commonly occurring order or progression in which the related entities or events take place.
-
B.
isContiguousState
Indicates that a state shares a continuous land border with the main body of the country, without being separated by foreign territory or significant bodies of water.
-
C.
hasContinuation
Indicates that one entity serves as a continuation or subsequent part of another entity in a sequence or process.
-
D.
hasRepresentationContinuity
Indicates that one entity maintains a consistent or continuous representational relationship with another across time, context, or state changes.
-
E.
canBeContinuous
Indicates that something has the potential to occur, exist, or be maintained without interruption over a continuous range or period.
- 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_69ca8436ba308190903e470776d2d893 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd7c8f59dc8190854dfc0d287731c6 |
completed | April 1, 2026, 8:14 p.m. |
| PD | Predicate disambiguation | batch_69cca550777c819094e1851a6127cbbc |
completed | April 1, 2026, 4:55 a.m. |
| PDg | Predicate description generation | batch_69cca89b3368819087a3d69270c1f185 |
completed | April 1, 2026, 5:09 a.m. |
Created at: March 30, 2026, 7:49 p.m.