Triple
T7906123
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Philippe Petit |
E183580
|
entity |
| Predicate | numberOfCrossings |
P52834
|
FINISHED |
| Object | multiple crossings between the Twin Towers |
—
|
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: multiple crossings between the Twin Towers | Statement: [Philippe Petit, numberOfCrossings, multiple crossings between the Twin Towers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfCrossings Context triple: [Philippe Petit, numberOfCrossings, multiple crossings between the Twin Towers]
-
A.
hadCrossingPoints
Indicates that two entities intersected or overlapped at one or more specific points in space or time.
-
B.
crossesBetween
Indicates that one entity passes from one side of a second entity to the other, traversing the space between two reference points or boundaries associated with that second entity.
-
C.
hasNumberOfCrosses
chosen
Indicates the quantity of crosses associated with or present on a given entity.
-
D.
hasCrossingPoint
Indicates that two or more entities intersect or share at least one common point in space or along their paths.
-
E.
crossingOf
Indicates that one entity serves as the intersection or crossing point of two or more linear features, such as roads, paths, or tracks.
- 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_69ca828dec0c81908b8f55a4dbbb53ff |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3a56c9f0819094dc87fe55a8823e |
completed | March 31, 2026, 3:07 a.m. |
| PD | Predicate disambiguation | batch_69cae92f9498819085277879e59aa072 |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 5:03 p.m.