Triple
T19875580
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shebaa Farms |
E477626
|
entity |
| Predicate | mappingHistory |
P137669
|
FINISHED |
| Object | French Mandate maps showed area as part of Syria |
—
|
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: French Mandate maps showed area as part of Syria | Statement: [Shebaa Farms, mappingHistory, French Mandate maps showed area as part of Syria]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mappingHistory Context triple: [Shebaa Farms, mappingHistory, French Mandate maps showed area as part of Syria]
-
A.
historicRoute
Indicates that a route has historical significance, typically due to its age, past usage, or role in notable events.
-
B.
nearHistoricalRoute
Indicates that one entity is located close to a historically significant route or pathway.
-
C.
historicalRouteName
Indicates that an entity has or is associated with a name it held historically as a route or pathway, distinct from its current official name.
-
D.
historicalLayer
Indicates a relationship where one entity represents or belongs to a particular historical stratum, phase, or period relative to another.
-
E.
movementHistory
Indicates a record of how an entity has moved over time, capturing the sequence, timing, and possibly locations of its movements.
- 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_69d8e51e7d948190aedbcd6c30361c39 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e658db058c8190b7bf0b003ead5bfc |
completed | April 20, 2026, 4:48 p.m. |
| PD | Predicate disambiguation | batch_69e537e8c4e481909fe95d795b4864e7 |
completed | April 19, 2026, 8:15 p.m. |
| PDg | Predicate description generation | batch_69e543c136b081909cab9394b958390a |
completed | April 19, 2026, 9:06 p.m. |
Created at: April 10, 2026, 1:52 p.m.